1,424 results on '"Image texture"'
Search Results
2. On the Model-Based Estimation of Backscatter Texture from SAR Image Texture for Area-Extensive Scenes
- Author
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Collins, M. J., Raney, R. K., and Livingstone, C. E.
- Published
- 1998
3. Image Texture Analysis Enhances Classification of Fire Extent and Severity Using Sentinel 1 and 2 Satellite Imagery.
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Gibson, Rebecca Kate, Mitchell, Anthea, and Chang, Hsing-Chung
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TEXTURE analysis (Image processing) , *SYNTHETIC aperture radar , *REMOTE-sensing images , *REMOTE sensing , *FIRE detectors , *FIRE , *VEGETATION monitoring , *RANDOM forest algorithms , *CLASSIFICATION - Abstract
Accurate and reliable mapping of fire extent and severity is critical for assessing the impact of fire on vegetation and informing post-fire recovery trajectories. Classification approaches that combine pixel-wise and neighbourhood statistics including image texture derived from high-resolution satellite data may improve on current methods of fire severity mapping. Texture is an innate property of all land cover surfaces that is known to vary between fire severity classes, becoming increasingly more homogenous as fire severity increases. In this study, we compared candidate backscatter and reflectance indices derived from Sentinel 1 and Sentinel 2, respectively, together with grey-level-co-occurrence-matrix (GLCM)-derived texture indices using a random forest supervised classification framework. Cross-validation (for which the target fire was excluded in training) and target-trained (for which the target fire was included in training) models were compared to evaluate performance between the models with and without texture indices. The results indicated that the addition of texture indices increased the classification accuracies of severity for both sensor types, with the greatest improvements in the high severity class (23.3%) for the Sentinel 1 and the moderate severity class (17.4%) for the Sentinel 2 target-trained models. The target-trained models consistently outperformed the cross-validation models, especially with regard to Sentinel 1, emphasising the importance of local training data in capturing post-fire variation in different forest types and severity classes. The Sentinel 2 models more accurately estimated fire extent and were improved with the addition of texture indices (3.2%). Optical sensor data yielded better results than C-band synthetic aperture radar (SAR) data with respect to distinguishing fire severity and extent. Successful detection using C-band data was linked to significant structural change in the canopy (i.e., partial-complete canopy consumption) and is more successful over sparse, low-biomass forest. Future research will investigate the sensitivity of longer-wavelength (L-band) SAR regarding fire severity estimation and the potential for an integrated fire-mapping system that incorporates both active and passive remote sensing to detect and monitor changes in vegetation cover and structure. [ABSTRACT FROM AUTHOR]
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- 2023
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4. Modeling Habitat Suitability for Greater Rheas Based on Satellite Image Texture
- Author
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Bellis, Laura M., Pidgeon, Anna M., Radeloff, Volker C., St-Louis, Véronique, Navarro, Joaquín L., and Martella, Mónica B.
- Published
- 2008
5. Integrating citizen science and multispectral satellite data for multiscale habitat management
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Van Eupen, Camille, Maes, Dirk, Heremans, Stien, Swinnen, Kristijn R. R., Somers, Ben, and Luca, Stijn
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- 2024
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6. High-resolution remotely sensed data characterizes indices of avifaunal habitat on private residential lands in a global metropolis
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Christian Benitez, Michael Beland, Sevan Esaian, and Eric M. Wood
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Image texture ,Land cover ,LiDAR ,Los Angeles ,NDVI ,Remote sensing ,Ecology ,QH540-549.5 - Abstract
Urban ecosystems are dominated by private lands which poses a significant hurdle to performing field-based assessment of wildlife. An alternative approach is to characterize indices of animal habitat in difficult-to-access areas using data from airborne remote sensing platforms. Characterizing indices of wildlife habitat using remotely sensed data is common in natural systems but has received less attention within urban ecosystems. We tested the utility of using remotely sensed data from high-resolution airborne sensors, including LiDAR, a measure of vertical habitat structure, NDVI, a measure of greenness, image texture, a measure of horizontal habitat structure, and parcel level land-cover data, along with field-based street-tree measurements to predict bird abundance and richness across Greater Los Angeles, California, USA. We surveyed birds and gathered street-tree data on public lands of residential neighborhoods and processed the remote sensing data in 50-m and 300-m circular buffers of bird survey locations to capture data primarily on private, residential land across three winter field seasons (2016–18, 2019/20) at 23 locations along a tree-canopy cover gradient. Data from LiDAR processed as an index for the density of trees summarized in the 50-m and 300-m extents were the strongest univariate predictors of avifaunal abundance and richness explaining 75 % and 74 % of the likelihood in fitted models. NDVI, image texture, land cover, and street-tree density measures were weaker univariate predictors than models fitted with LiDAR data. Models including LiDAR and ground-based street-tree measurements accounted for upwards of 80 % of the variability in avifaunal abundance and richness, particularly for bird species associated with trees and shrubs. We recommend the prioritization of high-resolution remote sensing data, particularly LiDAR, in combination with field-based habitat measures e.g., street trees, to characterize indices of avifaunal habitat on public and private lands of cities, which could help to improve our understanding of the distribution of birds across urban areas.
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- 2024
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7. Using Image Texture and Spectral Reflectance Analysis to Detect Yellowness and Esca in Grapevines at Leaf-Level
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H. Al-Saddik, Anthony Laybros, Frédéric Cointault, Bastien Billiot, Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté [COMUE] (UBFC), Groupe Roullier, Université de Bourgogne (UB)-Institut National de la Recherche Agronomique (INRA)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université Bourgogne Franche-Comté ( UBFC )
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PROSPECT ,[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing ,010504 meteorology & atmospheric sciences ,spectra ,[SDV]Life Sciences [q-bio] ,co-occurrence matrix ,0211 other engineering and technologies ,02 engineering and technology ,vineyard ,biophysical parameters ,01 natural sciences ,Vineyard ,diseases ,Digital image ,Image texture ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture ,texture ,classification ,lcsh:Science ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,[ SDV ] Life Sciences [q-bio] ,[ SDV.SA.STA ] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture ,Hyperspectral imaging ,Plant disease ,Co-occurrence matrix ,General Earth and Planetary Sciences ,Downy mildew ,Environmental science ,lcsh:Q ,Powdery mildew - Abstract
International audience; Plant diseases are one of the main reasons behind major economic and production losses in the agricultural field. Current research activities enable large fields monitoring and plant disease detection using innovative and robust technologies. French grapevines have a reputation for producing premium quality wines, however, these major fruit crops are susceptible to many diseases, including Esca, Downy mildew, Powdery mildew, Yellowing, and many others. In this study, we focused on two main infections (Esca and Yellowing), and data were gathered from fields that were located in Aquitaine and Burgundy regions, France. Since plant diseases can be diagnosed from the properties of the leaf, we acquired both Red-Green-Blue (RGB) digital image and hyperspectral reflectance data from infected and healthy leaves. Biophysical parameters that were produced by the PROSPECT model inversion together with texture parameters compiled from the literature were deduced. Then we investigated their relationship to damage caused by Yellowing and Esca. This study examined whether spectral and textural data can identify the two diseases through the use of Neural Networks. We obtained an overall accuracy of 99% for both of the diseases when textural and spectral data are combined. These results suggest that, first, biophysical parameters present a valid dimension reduction tool that could replace the use of complete hyperspectral data. Second, remote sensing using spectral reflectance and digital images can make an overall nondestructive, rapid, cost-effective, and reproducible technique to determine diseases in grapevines with a good level of accuracy.
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- 2018
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8. Population Density and Image Texture: A Comparison Study.
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Liu, XiaoHang, Clarke, Keith, and Herold, Martin
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POPULATION density ,LAND use ,REMOTE sensing ,NUMERICAL analysis ,AEROSPACE telemetry ,INFORMATION storage & retrieval systems ,CENSUS ,DEMOGRAPHIC surveys ,REMOTE-sensing images - Abstract
The correlation between census population density and Ikonos image texture was explored. The spatial unit for the analysis was census blocks with homogenous land-use. Ikonos image texture was described using three methods: the gray-level co-occurrence matrix (GLCM), semi-variance, and spatial metrics. Linear regression was conducted to explore the correlation between image texture and population density. It was found that although correlation exists, its degree varies depending on the method used to describe image texture. The highest correlation is given by the spatial metrics method. This result suggests that the correlation between texture and population density is not strong enough to predict or forecast residential population. However, image texture does provide a base to refine census-reported population distribution using remote sensing. High-resolution satellite images therefore have the potential to support "smart interpolation" programs to estimate human population distribution in areas where detailed information is not available. [ABSTRACT FROM AUTHOR]
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- 2006
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9. Classification of forest structure using very high resolution Pleiades image texture
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Benoit Beguet, Dominique Guyon, Samia Boukir, Nesrine Chehata, Université Sciences et Technologies - Bordeaux 1, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Institut Polytechnique de Bordeaux (Bordeaux INP), Institut de recherche pour le développement (IRD [Tunisie]), and IEEE Geoscience and Remote Sensing Society (GRSS). USA.
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010504 meteorology & atmospheric sciences ,Computer science ,Multispectral image ,0211 other engineering and technologies ,Pléiades ,02 engineering and technology ,15. Life on land ,01 natural sciences ,Texture (geology) ,Random forest ,Panchromatic film ,Tree (data structure) ,forest ,feature selection ,Image texture ,classification ,Image resolution ,texture ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
International audience; The potential of very high resolution Pléiades image texture for forest structure mapping was assessed on maritime pine stands in south-western France. A preliminary step showed that multi-linear regressions allow a reliable prediction of forest variables (such as crown diameter or tree height) from a set of features automatically selected among a huge number of Haralick texture features with various spatial parameterizations. In a second step, to assess Pléiades image texture contribution for classification, Random Forests (RF) classification was performed to discriminate four forest structure classes from recent reforestation to mature stand. Two texture feature selection strategies are compared: (1) the previous regression-based modelling using in situ tree measurements (2) the RF-variable importance using a visual photo-interpretation. Both methods produced comparable classification accuracies. The results highlight the contribution of processes automation and the need for using both Pléiades image resolutions (panchromatic and multispectral) to derive the best performing texture features.
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- 2014
10. Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix
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P. Dubois-Fernandez, Arnaud Alborini, Jean Costa, I. Champion, Christian Germain, Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA), Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), ONERA - The French Aerospace Lab [Châtillon], ONERA-Université Paris Saclay (COmUE), Écologie fonctionnelle et physique de l'environnement (EPHYSE - UR1263), and ONERA
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Synthetic aperture radar ,Forest management ,forestry ,15. Life on land ,radar applications ,synthetic aperture radar (SAR) ,Geotechnical Engineering and Engineering Geology ,Rendering (computer graphics) ,Correlation ,Co-occurrence matrix ,remote sensing ,Image texture ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[SDV.SA.SF]Life Sciences [q-bio]/Agricultural sciences/Silviculture, forestry ,Radar imaging ,image texture analysis ,Biomass ,Electrical and Electronic Engineering ,Image resolution ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Remote sensing ,Mathematics - Abstract
International audience; Data on forest variables (e.g., biomass, trunk height, density) are necessary for environmental and forest management applications. It has been shown that texture can be used instead of the usual σ/age relationships at P-band to retrieve plantation forest parameters, but the analysis of σ spatial characteristics has not been fully explored. The aim of this letter is to investigate the relationships between stand age (which is correlated to forest variables) and texture descriptors calculated from statistics generated by the gray-level co-occurrence matrix for varying distance d, and orientation α, values used to calculate the matrix. Synthetic aperture radar images are P-band airborne data acquired by the ONERA RAMSES instrument over a controlled homogeneous test site located in the Landes region, France. It is found that texture descriptors contrast, inverse difference moment, homogeneity, and correlation are strongly influenced by the parameters (d, α) related to forest stand structure (forest rows, stand density) and image resolution. In contrast, energy and entropy are observed to be highly correlated to stand age and displayed a stable performance whatever the distance and orientation parameters (d, α), thus rendering them a good contender as an alternative to the usual σ based relationships applied to this type of forest.
- Published
- 2014
11. Spatial Metrics and Image Texture for Mapping Urban Land Use.
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Herold, Martin, XiaoHang Liu, and Clarke, Keith C.
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ARTIFICIAL satellites ,REMOTE sensing ,REMOTE-sensing images ,URBAN land use - Abstract
The arrival of new-generation, high-spatial-resolution satellite imagery (e.g., Ikonos) has opened up new opportunities for detailed mapping and analysis of urban land use. Drawing on the traditional approach used in aerial photointerpretation, this study investigates an "object-oriented" method to classify a large urban area into detailed land-use categories. Spatial metrics and texture measures are used to describe the spatial characteristics of land-cover objects within each land-use region as derived from interpreted aerial photographs. In assessing how land-use categories vary in their spatial configuration, spatial metrics were found to provide the most important information for differentiating urban land uses. A detailed land-use map with nine categories was derived for the Santa Barbara South Coast Region area. Results from our work suggest that the region-based method exploiting spatial metrics and texture measurements is a potential new avenue to extract detailed urban land-use information from highresolution satellite imagery. [ABSTRACT FROM AUTHOR]
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- 2003
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12. Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands
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Peter M. Atkinson, Suha Berberoglu, Paul J. Curran, Anıl Akın, and Çukurova Üniversitesi
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Cohen's kappa ,Image texture ,Thematic Mapper ,G1 ,General Earth and Planetary Sciences ,Plant cover ,Image processing ,Land cover ,Variogram ,Cartography ,Geology ,Change detection ,Remote sensing - Abstract
Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting landuse/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985,1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the 'from-to' change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985-1993 and 1993-2005 image pairs, respectively. © 2010 Taylor & Francis.
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- 2010
13. Estimation of tropical forest biomass with image texture of radar images
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Champion, Isabelle, Da Costa, Jean-Pierre, Godineau, Adrien, Villard, Ludovic, Dubois-Fernandez, Pascale, Le Toan, Thuy, Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA), Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ONERA - The French Aerospace Lab [Salon], ONERA, Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
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analyse d'images ,remote sensing ,biomass ,télédétection ,[SDV]Life Sciences [q-bio] ,[SDE]Environmental Sciences ,forestry ,image texture ,radar - Abstract
International audience; Interest in the world forests has grown to unprecedented heights, especially with growing awareness of their role in the global carbon cycle. Quantifying carbon in forests is therefore of crucial importance for estimating carbon fluxes at the regional and global scale. Carbon quantities are estimated by inferring wood biomass from forest biomass, and then converting it into carbon by using a value of approximately 0.5 ton of carbon for 1 ton of wood. In order to determine the bio-mass of a forest, significant relationships have therefore been established between radar mean intensity and biophysical variables. However, for mature stands (about 80 t/ha and more) increasing biomass reduces the sensitivity of the backscattering coefficient sigma/biomass relationships. Recent studies have shown that texture could be used instead of the usual intensity-age relationships, even for mature stands up to 140 t/ha, the highest biomass value observed for studied forests (monospecific, even-aged forest, subject to identical silvicultural practices and sampling covering all forest stages from sowing to harvest). The present paper aims at extending these observations to tropical forests which is a large component of the terrestrial carbon pool and the carbon sources generated by deforestation in the tropics. Radar images at P-Band were acquired during the TropiSAR experiment in 2009 over the Paracou experimental site with the SETHI ONERA airborne instrument. Paracou is located in a lowland tropical rain forest near Sinnamary, French Guiana where 15 permanent plots of 6.25 ha each were mapped and regularly measured. Three sets of treatments applied to the 15 forest stands provide biomass values from 260 to 470 T/ha. Plots were selected inside the 15th experimental stands with paying attention to the local topogra-phy. Plots with similar slopes were thus compared. Statistical features were then derived a) from gray level statistics (mean sigma, variance, skewness...) and b) the statistics of pixel pairs (energy, contrast, correlation...) for each plot on the basis of the gray level co-occurrence matrix. It is shown for radar images at P-band and polarisation HV that despite the very homogeneous shape of this regenerating forest, linear relationships between some statistical features and forest biomass can be established which does not saturate even for biomass of more than 350 t/ha. These preliminary results are encouraging and further analysis should be carried out to explore the influence of the different treatments on the retrieval performance.
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- 2012
14. UAV Flight Height Impacts on Wheat Biomass Estimation via Machine and Deep Learning
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Wanxue Zhu, Ehsan Eyshi Rezaei, Hamideh Nouri, Zhigang Sun, Jing Li, Danyang Yu, and Stefan Siebert
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Agriculture ,image resolution ,image texture analysis ,remote sensing ,spectral analysis ,Ocean engineering ,TC1501-1800 ,Geophysics. Cosmic physics ,QC801-809 - Abstract
Optical unmanned aerial vehicle (UAV) remote sensing is widely prevalent to estimate crop aboveground biomass (AGB). Nevertheless, limited knowledge of the UAV flight height (mainly characterized by different image numbers and spatial resolutions) influences the crop AGB estimation accuracy across diverse sensing datasets and machine-/deep-learning models. This article assessed the impacts of flight height and integration of multiscale sensing information on wheat AGB estimation. The multispectral UAV flight missions with 30, 60, 90, and 120 m heights were conducted at the wheat grain filling phase in 2018 and 2019. To estimate AGB, we used the UAV-based crop surface model (CSM), spectral, texture indices, and their combinations along with a deep convolutional neural network (DCNN with AlexNet architecture), random forest, and support vector machine models. Results showed the CSM and textures exhibit sensitivity to flight height, with estimation accuracy declining by 48% and 41%, respectively, as the flight height increased from 30 to 120 m. Spectral indices displayed lesser sensitivity with accuracy decrease of 25%. Integrating data from different heights exhibited better performances in texture and spectral indices while reducing performance when CSM was input. The DCNN performed best particularly at high spatial image scales, whereas more sensitive to flight height, as the AGB estimation accuracy decreased by 30% and 47% from 30 to 120 m for machine learning and DCNN, respectively. Integrating texture and spectral information derived from images with moderate spatial resolutions (4–6 cm), and the integration of multiscale textures, are optimal for grain-filling wheat AGB estimation.
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- 2023
- Full Text
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15. High-resolution Remote Sensing Image Semi-global Matching Method Considering Geometric Constraints of Connection Points and Image Texture Information.
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Jingguo LYU, Xingbin YANG, Danlu ZHANG, and Shan JIANG
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REMOTE-sensing images ,IMAGE registration ,HIGH resolution imaging ,COMPUTER algorithms ,REMOTE sensing ,TEXTURE analysis (Image processing) ,TEXTURE mapping - Abstract
Dense matching of remote sensing images is a key step in the generation of accurate digital surface models. The semi-global matching algorithm comprehensively considers the advantages and disadvantages of local matching and global matching in terms of matching effect and computational efficiency, so it is widely used in close-range, aerial and satellite image matching. Based on the analysis of the original semi-global matching algorithm, this paper proposes a semi-global high-resolution remote sensing image that takes into account the geometric constraints of the connection points and the image texture information based on a large amount of high-resolution remote sensing image data and the characteristics of clear image texture. The method includes 4 parts; (T) Precise orientation. Aiming at the system error in the image orientation model, the system error of the rational function model is compensated by the geometric constraint relationship of the connecting points between the images, and the sub-pixel positioning accuracy is obtained; (2) Epipolar image generation. After the original image is divided into blocks, the epipolar image is generated based on the projection trajectory method; (3) Image dense matching. In order to reduce the size of the cost space and calculation time, the image is pyramided and then semi-globally matched layer by layer. In the matching process, the disparity map expansion and erosion algorithm that takes into account the image texture information is introduced to restrict the disparity search range and better retain the edge characteristics of the ground objects; @ Generate DSM. In order to test the matching effect, the weighted median filter algorithm is used to filter the disparity map, and the DSM is obtained based on the principle of forward intersection. Finally, the paper uses the matching results of WordView3 and Ziyuan No. 3 stereo image to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]
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- 2021
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16. A new algorithm for remotely sensed image texture classification and segmentation.
- Author
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Wang, Yao-Wei, Wang, Yan-Fei, Xue, Yong, and Gao, Wen
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- *
REMOTE sensing , *DETECTORS , *ELECTRONIC surveillance , *ALGORITHMS , *ALGEBRA , *IMAGE analysis - Abstract
In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications. [ABSTRACT FROM AUTHOR]
- Published
- 2004
- Full Text
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17. Satellite image texture for the assessment of tropical anuran communities.
- Author
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Sugai, Larissa Sayuri Moreira, Sugai, José Luiz Massao Moreira, Ferreira, Vanda Lucia, and Silva, Thiago Sanna Freire
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REMOTE-sensing images ,SAVANNAS ,TEXTURES ,SPECIES diversity ,SAVANNA ecology ,COMMUNITIES ,FOREST canopy gaps - Abstract
Copyright of Biotropica is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2019
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18. A New Image Texture Segmentation Based on Contourlet Fractal Features.
- Author
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Sarafrazi, Katayoon, Yazdi, Mehran, and Abedini, Mohammad Javad
- Subjects
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IMAGE processing , *REMOTE sensing , *FRACTALS , *IMAGING systems , *FUZZY sets - Abstract
Texture segmentation is one of the most difficult and important tasks in the remote sensing image processing. Through the years, several methods, based on statistical features, structural features and other image features, have been proposed for texture segmentation. This paper proposes a new feature based on Contourlet transform and Fractal analysis for texture segmentation. The performance of the proposed feature is compared with that of fractal, statistical, fuzzy and power spectrum features in presence of noise and the obtained results show superiority of the new proposed feature. [ABSTRACT FROM AUTHOR]
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- 2013
- Full Text
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19. Using remote sensing to identify soil types based on multiscale image texture features.
- Author
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Duan, Mengqi and Zhang, Xiaoguang
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REMOTE sensing , *DIGITAL soil mapping , *SOIL mapping , *TEXTURES - Abstract
• The addition of texture features could improve the accuracy of soil interpretations with remote sensing. • The optimal single-scale window size of the feature parameters was extracted. • The optimal multiscale combination of window sizes for each parameter were determined. • The multiscale fusion of texture feature windows was better than the single-scale window in soil mapping. Studying the spatial distribution of soil types is an important academic and practical issue in agriculture. With the rapid development of remote sensing technology, the role of image texture as an auxiliary variable in remote sensing identification of objects has increased. It is of great importance to ascertain the optimal window size for extracting texture features and the multiscale fusion of texture feature parameters under the optimal window for different soil types. To reach this goal, soil types in a typical area of the Jiaodong Peninsula were selected as the subject investigated, homogeneity and entropy were selected as the two texture feature parameters, and the ability to identify the different soil types based on the textural features was systematically analyzed by using Landsat 8 remote sensing images. Moreover, the optimal window sizes for extracting texture features were determined, and the role of multiscale textural features in the classification of the soil types was also evaluated. The results show that the accuracy of classification significantly increased with the addition of textural features. The optimal single-scale window sizes for the homogeneity and entropy feature parameters were 19 × 19 and 21 × 21, respectively. The fusion of multiscale textural features further improved the classification accuracy. The optimal multiscale window sizes for the homogeneity were 7 × 7, 13 × 13, 19 × 19 and 21 × 21 and those for entropy were 5 × 5, 15 × 15, 21 × 21 and 23 × 23. Therefore, the method of using texture information in remote sensing images as auxiliary variables in digital soil mapping was feasible. The method of multiscale fusion of texture features, which resulted in greater classification accuracy, was better than that of single-scale window. These conclusions could play an important guiding role in soil digital mapping with remote sensing. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
20. Radar image texture as a function of forest stand age
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P. Dubois-Fernandez, M. Cottrel, I. Champion, Dominique Guyon, Écologie fonctionnelle et physique de l'environnement (EPHYSE), Institut National de la Recherche Agronomique (INRA), ONERA - The French Aerospace Lab [Salon], and ONERA
- Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other ,Biomass (ecology) ,010504 meteorology & atmospheric sciences ,Forest management ,0211 other engineering and technologies ,Sampling (statistics) ,02 engineering and technology ,Vegetation ,15. Life on land ,01 natural sciences ,Texture (geology) ,law.invention ,law ,Radar imaging ,General Earth and Planetary Sciences ,Physical geography ,Radar ,Silviculture ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Mathematics ,Remote sensing - Abstract
International audience; Data on forest variables are required for environmental and forest management applications. Numerous authors have shown significant correlations between mean radar response intensity and forest variables (age, height or biomass) but few studies have explored the spatial characteristics of the radar response for varying forest states. In this Letter, variation in the most commonly used texture features is shown as a function of an indicator of forest growth (age) for a controlled homogeneous test site (monospecific, even-aged forest, with identical sylvicultural practices and a sampling that covers all forest stages from sowing to harvest). Significant linear relationships between some texture features and stand age are observed. Moreover, the quality of some fits indicates that texture could be used instead of the usual intensity-age relationships that saturate for mature stands.
- Published
- 2008
21. Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions
- Author
-
Cutler, M.E.J., Boyd, D.S., Foody, G.M., and Vetrivel, A.
- Subjects
- *
FOREST biomass , *TEXTURE analysis (Image processing) , *LANDSAT satellites , *PREDICTION models , *ARTIFICIAL neural networks , *GLOBAL environmental change , *ESTIMATION theory , *BIOTIC communities , *REMOTE sensing - Abstract
Abstract: Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r =0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r =0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). [Copyright &y& Elsevier]
- Published
- 2012
- Full Text
- View/download PDF
22. Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery
- Author
-
Kayitakire, F., Hamel, C., and Defourny, P.
- Subjects
- *
REMOTE sensing , *FOREST surveys , *LANDSAT satellites , *FORESTS & forestry - Abstract
Abstract: Remote sensing techniques have been seen as valuable and low-cost tools for frequent forest inventory purposes. However, estimation errors of relevant forest structure variables remain too high for operational use of high spatial resolution satellite imagery, such as Landsat TM/ETM and SPOT HRV, in temperate forests. Very high spatial resolution images that have been acquired by new commercial satellites, such as IKONOS-2 or QuickBird, are expected to reduce estimation errors to a level that is acceptable by foresters. This study assessed the capability of 1-m resolution IKONOS-2 imagery to estimate the five main forest variables—age, top height, circumference, stand density and basal area—in even-aged common spruce stands. They were estimated on the basis of texture features that were derived from the grey-level co-occurrence matrix (GLCM). The coefficients of determination, R 2, of the best models ranged from 0.76 to 0.82 for top height, circumference, stand density and age variables. Basal area was found to be weakly correlated to texture variables (R 2 =0.35). Relative prediction errors of four out of the five studied forest variables were comparable to the usual sampling inventory errors (top height: 10%; circumference: 15%; basal area: 16%; age: 18%), but the stand density estimation error (29%) remained too high for use in forest planning. The sensitivity analysis to the GLCM parameters showed that the most important parameters were the texture feature, the displacement and the window size. The orientation parameter had minimal effects on the R 2 values, even if it influenced the values of the texture features. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
- View/download PDF
23. Preserving boundaries for image texture segmentation using grey level co-occurring probabilities
- Author
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Jobanputra, Rishi and Clausi, David A.
- Subjects
- *
DIGITAL image processing , *DIGITAL electronics , *RESEARCH , *PROBABILITY theory - Abstract
Abstract: Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
- View/download PDF
24. Rapid extraction of image texture by co-occurrence using a hybrid data structure
- Author
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Clausi, David A. and Zhao, Yongping
- Subjects
- *
DIGITAL photography , *REMOTE sensing , *PROBABILITY theory - Abstract
Calculation of co-occurrence probabilities is a popular method for determining texture features within remotely sensed digital imagery. Typically, the co-occurrence features are calculated by using a grey level co-occurrence matrix (GLCM) to store the co-occurring probabilities. Statistics are applied to the probabilities in the GLCM to generate the texture features. This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities when applying the statistics. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. An improvement on the GLCLL is to utilize a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL.The GLCHS method is implemented using the C language in a Unix environment. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4% (
σ=3.08% ) of the computational time required by the GLCLL. Significant computational gains are made using the GLCHS method. [Copyright &y& Elsevier]- Published
- 2002
- Full Text
- View/download PDF
25. Performance evaluation of pansharpening Sentinel 2A imagery for informal settlement identification by spectral-textural features.
- Author
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Matarira, D., Mutanga, O., and Naidu, M.
- Subjects
- *
MULTISPECTRAL imaging , *TEXTURE analysis (Image processing) , *FEATURE extraction , *SUPPORT vector machines - Abstract
The diversity of informal settlement morphologies across locales makes their mapping inherently challenging in heterogeneous urban landscapes. The aim of this study was to evaluate the potential of pansharpening techniques on Sentinel 2A data, and textural features, in enhancing informal settlement identification accuracy in a fragmented urban environment. Brovey transform, intensity, hue and saturation transform, Environmental Systems Research Institute (ESRI), simple mean, and Gram–Schmidt techniques were employed to pansharpen multispectral bands of Sentinel 2A, bands 5, 6, and 7 in the first group, and bands 8A, 11 and 12 in another, using an average of bands 4 and 8 as the panchromatic band. The main objective was to investigate the efficacy of pansharpening Sentinel 2A imagery and texture analysis in automated mapping of morphologically varied informal settlements. An evaluation of the quality of fused images was undertaken through computation of the correlation between the spectral values of the original multispectral and pansharpened image. Grey-level-co-occurrence matrix texture features were extracted from the pansharpened images, and subsequently incorporated in the classification process, using a support vector machine classifier. Our results confirm that the Gram–Schmidt fusion technique yielded the highest informal settlement identification accuracy (F-score 95.2%; overall accuracy 91.8%). The experimental results demonstrated the potential of pansharpening Sentinel 2A, and the added value of image texture for a more nuanced characterisation of informal settlements. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
26. Roughness Analysis of Sea Surface From Visible Images by Texture
- Author
-
Peilin Gao, Hailang Pan, Ruixue Ma, Xin Zhang, Jingsong Yang, and Huicheng Zhou
- Subjects
010504 meteorology & atmospheric sciences ,General Computer Science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,Surface finish ,01 natural sciences ,Texture (geology) ,Wind speed ,gray level-gradient co-occurrence matrices ,Image texture ,autocorrelation function ,Surface roughness ,General Materials Science ,gray level co-occurrence matrices ,0105 earth and related environmental sciences ,Remote sensing ,ComputingMethodologies_COMPUTERGRAPHICS ,Fractional Brownian motion ,Autocorrelation ,General Engineering ,tamura texture features ,021001 nanoscience & nanotechnology ,Computer Science::Graphics ,Feature (computer vision) ,edge frequency ,Computer Science::Computer Vision and Pattern Recognition ,Sea surface roughness ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,0210 nano-technology ,lcsh:TK1-9971 ,Geology - Abstract
This paper presents a roughness analysis of sea surface from visible images by feature measurements of texture for the first time. The algorithms presented in this paper include six texture feature measurements of sea surface use gray level co-occurrence matrix, gray level-gradient co-occurrence matrix, Tamura texture feature, autocorrelation function, edge frequency and fractional Brownian motion autocorrelation. The empirical relationship between wind speeds (or sea surface roughness) and image texture roughness are estimated based on the extracted data. Our experiments have demonstrated that our texture methods and empirical relation between wind speeds and image texture roughness can potentially be used to analyze sea surface roughness from visible images.
- Published
- 2020
27. Estimating biomass of mixed and uneven-aged forests using spectral data and a hybrid model combining regression trees and linear models
- Author
-
López-Serrano Pablito M, López-Sánchez Carlos A, Díaz-Varela Ramón A, Corral-Rivas José J, Solís-Moreno R, Vargas-Larreta B, and Álvarez-González Juan G
- Subjects
Regression Trees ,Stepwise Regression ,Remote Sensing ,ATCOR3 ,Terrain Features ,Image Texture ,Forestry ,SD1-669.5 - Abstract
The Sierra Madre Occidental mountain range (Durango, Mexico) is of great ecological interest because of the high degree of environmental heterogeneity in the area. The objective of the present study was to estimate the biomass of mixed and uneven-aged forests in the Sierra Madre Occidental by using Landsat-5 TM spectral data and forest inventory data. We used the ATCOR3® atmospheric and topographic correction module to convert remotely sensed imagery digital signals to surface reflectance values. The usual approach of modeling stand variables by using multiple linear regression was compared with a hybrid model developed in two steps: in the first step a regression tree was used to obtain an initial classification of homogeneous biomass groups, and multiple linear regression models were then fitted to each node of the pruned regression tree. Cross-validation of the hybrid model explained 72.96% of the observed stand biomass variation, with a reduction in the RMSE of 25.47% with respect to the estimates yielded by the linear model fitted to the complete database. The most important variables for the binary classification process in the regression tree were the albedo, the corrected readings of the short-wave infrared band of the satellite (2.08-2.35 µm) and the topographic moisture index. We used the model output to construct a map for estimating biomass in the study area, which yielded values of between 51 and 235 Mg ha-1. The use of regression trees in combination with stepwise regression of corrected satellite imagery proved a reliable method for estimating forest biomass.
- Published
- 2016
- Full Text
- View/download PDF
28. Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran
- Author
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Ahmad Maleknezhad Yazdi, Vahid Eisavi, and Ali Shahsavari
- Subjects
Hyperspectral imagery ,image texture ,GLCM ,remote sensing ,SVM classification. ,Forestry ,SD1-669.5 - Abstract
Due to rapid population growth over recent decades, changes of urban areas have significantly impacted the environment. Urban is a heterogeneous and highly fragmented environment which has made them a challenging area for remote sensing imagery. The reliability of the information delivered by remote sensing applications in urban area highly depends on the quality of spatial and spectral data. Accordingly, the objective of this study is to analyze the impact of incorporation of Hyperion imagery and textural characteristics of high resolution panchromatic ALI imagery in classifying of urban region of south west of Tehran. To this end, we extracted textural information from panchromatic ALI imagery using gray-level co-occurrence matrix (GLCM) method. Classification was carried out by SVM method in five scenarios: Classification of spectral band of CNT method, classification of spectral bands plus texture with window size 3, size 5, size 7 and size 9. The classification results show that the urban areas of south west of Tehran are insufficiently characterized by the Hyperion satellite imagery. A quantitative assessment of the results demonstrated that the use of texture information improved urban land covers classification. As a result, combining of texture information with Hyperion imagery decreases class confusion specifically in heterogonous classes. The GLCM features show great potential for land use cover classification in heterogeneous areas with rich textural information.
- Published
- 2016
- Full Text
- View/download PDF
29. High-resolution remotely sensed data characterizes indices of avifaunal habitat on private residential lands in a global metropolis.
- Author
-
Benitez, Christian, Beland, Michael, Esaian, Sevan, and Wood, Eric M.
- Subjects
- *
URBAN ecology , *HABITATS , *REMOTE sensing , *METROPOLIS , *NEIGHBORHOODS - Abstract
• Cities are dominated by private lands which presents a challenge for field study. • LiDAR data characterizes indices of avifaunal habitat on private residential lands. • NDVI, image texture, and land cover data are weaker predictors of urban avifauna. • LiDAR combined with street tree data has the strongest predictive power for urban birds. • High-resolution remote sensing data should be utilized in urban avifaunal studies. Urban ecosystems are dominated by private lands which poses a significant hurdle to performing field-based assessment of wildlife. An alternative approach is to characterize indices of animal habitat in difficult-to-access areas using data from airborne remote sensing platforms. Characterizing indices of wildlife habitat using remotely sensed data is common in natural systems but has received less attention within urban ecosystems. We tested the utility of using remotely sensed data from high-resolution airborne sensors, including LiDAR, a measure of vertical habitat structure, NDVI, a measure of greenness, image texture, a measure of horizontal habitat structure, and parcel level land-cover data, along with field-based street-tree measurements to predict bird abundance and richness across Greater Los Angeles, California, USA. We surveyed birds and gathered street-tree data on public lands of residential neighborhoods and processed the remote sensing data in 50-m and 300-m circular buffers of bird survey locations to capture data primarily on private, residential land across three winter field seasons (2016–18, 2019/20) at 23 locations along a tree-canopy cover gradient. Data from LiDAR processed as an index for the density of trees summarized in the 50-m and 300-m extents were the strongest univariate predictors of avifaunal abundance and richness explaining 75 % and 74 % of the likelihood in fitted models. NDVI, image texture, land cover, and street-tree density measures were weaker univariate predictors than models fitted with LiDAR data. Models including LiDAR and ground-based street-tree measurements accounted for upwards of 80 % of the variability in avifaunal abundance and richness, particularly for bird species associated with trees and shrubs. We recommend the prioritization of high-resolution remote sensing data, particularly LiDAR, in combination with field-based habitat measures e.g., street trees, to characterize indices of avifaunal habitat on public and private lands of cities, which could help to improve our understanding of the distribution of birds across urban areas. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
30. Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring
- Author
-
Jie Fang, Shengjun Xu, Xiaoqian Cao, and Dianwei Wang
- Subjects
Atmospheric Science ,Deblurring ,Computer science ,QC801-809 ,Kernel density estimation ,Geophysics. Cosmic physics ,0211 other engineering and technologies ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Multi-task learning ,02 engineering and technology ,Domain transformation ,image deblurring ,Domain (software engineering) ,Ocean engineering ,Image texture ,Kernel (image processing) ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Computers in Earth Sciences ,multitask learning mechanism ,TC1501-1800 ,Image restoration ,021101 geological & geomatics engineering ,Remote sensing - Abstract
As a fundamental preprocessing technique, remote sensing image motion deblurring is important for visual understanding tasks. Most conventional approaches formulate the image motion deblurring task as a kernel estimation. Because the kernel estimation is a highly ill-posed problem, many priors have been applied to model the images and kernels. Even though these methods have obtained relatively better performances, they are usually time-consuming and not robust for different conditions. To address this problem, we propose a multitask learning mechanism for remote sensing image motion deblurring in this article, which contains an image restoration subtask and an image texture complexity recognition one. First, we consider the image motion deblurring problem as a domain transformation problem, from the blurred domain to a clear one. Specifically, the blurred domain represents the data space consisted of blurring images, and the definition of clear domain is similar. Second, we design a novel weighted attention map loss to enhance the reconstruction capability of the restoration subbranch for difficult local regions. Third, based on the restoration subbranch, a recognition subbranch is incorporated into the framework to guide the deblurring process, which provides the auxiliary texture complexity information to help the optimization of restoration subbranch. Additionally, in order to optimize the proposed network, we construct three large-scale datasets, and each sample in the dataset contains a clear image, a blurred image, and its texture label obtained by corresponding texture complexity. Finally, the experimental results on three constructed datasets demonstrate the robustness and the effectiveness of the proposed method.
- Published
- 2021
31. Image Restoration Network Under Complex Meteorological Environment: GRASPP-GAN
- Author
-
Ma Jingyi, Yang Bin, Jing Guodong, Zhang Tiejun, and Yan Wenjun
- Subjects
Ground truth ,Discriminator ,General Computer Science ,Computer science ,Feature extraction ,General Engineering ,Training (meteorology) ,GAN ,TK1-9971 ,Image restoration ,Image texture ,General Materials Science ,Pyramid (image processing) ,rain ,Electrical engineering. Electronics. Nuclear engineering ,meteorology ,Rain and snow mixed ,Remote sensing - Abstract
The color and contrast of objects in the image will be affected by meteorological factors, especially rain and snow will block part of the image, which will change the information contained in the image. Image restoration under bad weather conditions has practical application value. At present, most of the research focuses on the removal of fog, and the research on complex rain and snow is relatively less. Rain has more prominent features in gradient domain, and it is more distinct from non-rain image texture. In this paper, Generative Adversarial Networks is used to combine the information of image in gradient domain and spatial domain to get better performance of rain removal. Gradient aided coding is used in the generator to generate depth features that are more conducive to rain removal. In the discriminator, the gradient is used as an additional input to provide more recognizable rain and non-rain information, which enhances the discriminator’s ability to distinguish the image generated by the generator and the ground truth. By modifying the network structure of the expanded spatial pyramid pooling (ASPP), the abnormal rain removal results produced by the generator are reduced. Experimental results show that the proposed method improves the performance of rain removal and the visual quality of the generated image.
- Published
- 2021
32. Image texture processing and data integration for surface pattern discrimination
- Author
-
Peddle, Derek R. and Franklin, Steven E.
- Subjects
REMOTE sensing - Published
- 1990
33. Comparisons between spectral mapping units derived from SPOT image texture and field soil map units
- Author
-
Nizeyimana, Egide and Agbu, Patrick A.
- Subjects
LAND use ,REMOTE sensing - Published
- 1990
34. HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS
- Author
-
D. Laupheimer, Norbert Haala, Philipp Glira, Michael Kölle, Michael Cramer, and Gottfried Mandlburger
- Subjects
lcsh:Applied optics. Photonics ,Computer science ,Orientation (computer vision) ,lcsh:T ,Automatic identification and data capture ,0211 other engineering and technologies ,Point cloud ,lcsh:TA1501-1820 ,02 engineering and technology ,lcsh:Technology ,Deformation monitoring ,Photogrammetry ,Lidar ,Image texture ,lcsh:TA1-2040 ,0202 electrical engineering, electronic engineering, information engineering ,Trajectory ,020201 artificial intelligence & image processing ,lcsh:Engineering (General). Civil engineering (General) ,021101 geological & geomatics engineering ,Remote sensing - Abstract
This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.
- Published
- 2020
35. Research on Optimization Methods of ELM Classification Algorithm for Hyperspectral Remote Sensing Images
- Author
-
Li Li, Fang Huang, Xicheng Tan, Jun Lu, Peng Liu, and Jian Tao
- Subjects
010504 meteorology & atmospheric sciences ,General Computer Science ,Computer science ,Hyperspectral remote sensing ,0211 other engineering and technologies ,02 engineering and technology ,01 natural sciences ,Bottleneck ,Image texture ,Feature (machine learning) ,ELM algorithm ,General Materials Science ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Extreme learning machine ,Remote sensing ,Contextual image classification ,business.industry ,Deep learning ,General Engineering ,Hyperspectral imaging ,deep learning ,Ensemble learning ,ComputingMethodologies_PATTERNRECOGNITION ,ensemble learning ,Artificial intelligence ,texture features ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,business ,Algorithm ,lcsh:TK1-9971 - Abstract
In land-use classification of hyperspectral remote sensing (RS) images, traditional classification methods often experience large amount of datasets and low efficiency. To solve these problems, a fast machine-learning method, the extreme learning machine (ELM) algorithm, was introduced. However, basic use of the ELM usually encounters problems of unstable classification results and low classification accuracy. Hence, in this paper, optimization methods for ELM-based RS image classification were mainly discussed and applied to solve the bottleneck problems. From the three perspectives of ensemble learning, making full use of image texture features, and deep learning, three classification optimization methods have been designed and implemented. The results show that: 1) To some extent, all the three methods can achieve a balance between classification accuracy and efficiency, i.e., they can maintain the advantage of ELM algorithm in classification efficiency and speed while have better classification accuracy; 2) The image texture feature optimization method (LBP-KELM) solves the problem of unsatisfactory classification results experienced by the ensemble learning optimization method (Ensemble-ELM) and further improves classification accuracy. However, the classification results are sensitive to the type of dataset; and 3) Fortunately, the optimization method combined with deep learning (CNN-ELM) can meet the application needs of multiple datasets. Furthermore, it can also further improve classification accuracy.
- Published
- 2019
36. A Combinatorial K-View Based Algorithm for Image Texture Classification.
- Author
-
Lan, Yihua, Ren, Haozheng, and Chen, Yi
- Abstract
Textural features is very important properties in many types of images. Partitioning an image into homogeneous regions based on textural features is useful in computer vision. Many texture classification algorithms have been proposed including Local Binary Patterns, Gray Level Co-Occurrence and K-View based algorithms, to name a few. Among of them, The K-View using Rotation-invariant feature algorithm (K-View-R) and the fast weighted K-View-Voting algorithm (K-View-V) produce higher classification accuracy by compare with those original K-View based algorithms. However, there still have some rooms for improvement. In this paper, by analyzing those K-View based algorithms, an attempt to utilize the advantages of the K-View-R and K-View-V was investigated. The new approach which we called combinatorial K-View based method was presented. To test and evaluate the proposed method, some experiments were carried out on a lot of textural images which taken from a standard database. Preliminary experimental results demonstrated the new method achieved more accurate classification by compare with other K-View based methods. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
37. Retrieval of Forest Stand Age From SAR Image Texture for Varying Distance and Orientation Values of the Gray Level Co-Occurrence Matrix.
- Author
-
Champion, Isabelle, Germain, Christian, Da Costa, Jean Pierre, Alborini, Arnaud, and Dubois-Fernandez, Pascale
- Abstract
Data on forest variables (e.g., biomass, trunk height, density) are necessary for environmental and forest management applications. It has been shown that texture can be used instead of the usual \sigmao/age relationships at P-band to retrieve plantation forest parameters, but the analysis of \sigmao spatial characteristics has not been fully explored. The aim of this letter is to investigate the relationships between stand age (which is correlated to forest variables) and texture descriptors calculated from statistics generated by the gray-level co-occurrence matrix for varying distance d, and orientation \alpha, values used to calculate the matrix. Synthetic aperture radar images are P-band airborne data acquired by the ONERA RAMSES instrument over a controlled homogeneous test site located in the Landes region, France. It is found that texture descriptors contrast, inverse difference moment, homogeneity, and correlation are strongly influenced by the parameters (d, \alpha) related to forest stand structure (forest rows, stand density) and image resolution. In contrast, energy and entropy are observed to be highly correlated to stand age and displayed a stable performance whatever the distance and orientation parameters (d, \alpha), thus rendering them a good contender as an alternative to the usual \sigmao based relationships applied to this type of forest. [ABSTRACT FROM PUBLISHER]
- Published
- 2014
- Full Text
- View/download PDF
38. STs-NeRF: Novel View Synthesis of Space Targets Based on Improved Neural Radiance Fields.
- Author
-
Ma, Kaidi, Liu, Peixun, Sun, Haijiang, and Teng, Jiawei
- Subjects
FOCAL length ,RADIANCE ,REMOTE-sensing images ,MICROSPACECRAFT ,SIGNAL-to-noise ratio - Abstract
Since Neural Radiation Field (NeRF) was first proposed, a large number of studies dedicated to them have emerged. These fields achieved very good results in their respective contexts, but they are not sufficiently practical for our project. If we want to obtain novel images of satellites photographed in space by another satellite, we must face problems like inaccurate camera focal lengths and poor image texture. There are also some small structures on satellites that NeRF-like algorithms cannot render well. In these cases, the NeRF's performance cannot sufficiently meet the project's needs. In fact, the images rendered by the NeRF will have many incomplete structures, while the MipNeRF will blur the edges of the structures on the satellite and obtain unrealistic colors. In response to these problems, we proposed STs-NeRF, which improves the quality of the new perspective images through an encoding module and a new network structure. We found a method for calculating poses that are suitable for our dataset and that enhance the network's input learning effect by recoding the sampling points and viewing directions through a dynamic encoding (DE) module. Then, we input them into our layer-by-layer normalized multi-layer perceptron (LLNMLP). By simultaneously inputting points and directions into the network, we avoid the mutual influence between light rays, and through layer-by-layer normalization, we ease the model's overfitting from a training perspective. Since real images should not be made public, we created a synthetic dataset and conducted a series of experiments. The experiments showed that our method achieves the best results in reconstructing captured satellite images, compared with the NeRF, the MipNeRF, the NeuS and the NeRF2Mesh, and improves the Peak Signal-to-Noise Ratio (PSNR) by 19%. We have also tested on public datasets, and our NeRF can still render acceptable images on datasets with better textures. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
39. Discrimination of Degraded Pastures in the Brazilian Cerrado Using the PlanetScope SuperDove Satellite Constellation.
- Author
-
Silva, Angela Gabrielly Pires, Galvão, Lênio Soares, Ferreira Júnior, Laerte Guimarães, Teles, Nathália Monteiro, Mesquita, Vinícius Vieira, and Haddad, Isadora
- Subjects
CERRADOS ,CONSTELLATIONS ,PASTURES ,BIODEGRADATION ,RANDOM forest algorithms ,REMOTE sensing - Abstract
Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
40. Full-Process Adaptive Encoding and Decoding Framework for Remote Sensing Images Based on Compression Sensing.
- Author
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Hu, Huiling, Liu, Chunyu, Liu, Shuai, Ying, Shipeng, Wang, Chen, and Ding, Yi
- Subjects
IMAGE compression ,REMOTE sensing ,COMPRESSED sensing ,IMAGE reconstruction ,ENCODING ,FEATURE extraction ,IMAGE segmentation - Abstract
Faced with the problem of incompatibility between traditional information acquisition mode and spaceborne earth observation tasks, starting from the general mathematical model of compressed sensing, a theoretical model of block compressed sensing was established, and a full-process adaptive coding and decoding compressed sensing framework for remote sensing images was proposed, which includes five parts: mode selection, feature factor extraction, adaptive shape segmentation, adaptive sampling rate allocation and image reconstruction. Unlike previous semi-adaptive or local adaptive methods, the advantages of the adaptive encoding and decoding method proposed in this paper are mainly reflected in four aspects: (1) Ability to select encoding modes based on image content, and maximizing the use of the richness of the image to select appropriate sampling methods; (2) Capable of utilizing image texture details for adaptive segmentation, effectively separating complex and smooth regions; (3) Being able to detect the sparsity of encoding blocks and adaptively allocate sampling rates to fully explore the compressibility of images; (4) The reconstruction matrix can be adaptively selected based on the size of the encoding block to alleviate block artifacts caused by non-stationary characteristics of the image. Experimental results show that the method proposed in this article has good stability for remote sensing images with complex edge textures, with the peak signal-to-noise ratio and structural similarity remaining above 35 dB and 0.8. Moreover, especially for ocean images with relatively simple image content, when the sampling rate is 0.26, the peak signal-to-noise ratio reaches 50.8 dB, and the structural similarity is 0.99. In addition, the recovered images have the smallest BRISQUE value, with better clarity and less distortion. In the subjective aspect, the reconstructed image has clear edge details and good reconstruction effect, while the block effect is effectively suppressed. The framework designed in this paper is superior to similar algorithms in both subjective visual and objective evaluation indexes, which is of great significance for alleviating the incompatibility between traditional information acquisition methods and satellite-borne earth observation missions. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
41. Texture analysis approaches in modelling informal settlements: a review.
- Author
-
Matarira, D., Mutanga, O., and Naidu, M.
- Subjects
- *
REMOTE sensing , *FOREST productivity , *SUPPORT vector machines , *DEEP learning , *FEATURE selection , *MACHINE learning - Abstract
Texture-based informal settlement (IS) mapping has gained attention in urban remote sensing (RS) research. Numerous studies conducted on the use of texture analysis for IS mapping have investigated wide-ranging sensors, algorithms, scale, classifiers, feature selection methods, and other factors of interest. However, no study has systematically investigated key factors affecting texture-based classification processes. This paper presents a detailed synthesis of scientific progress in texture based IS mapping. Results revealed that grey level co-occurrence matrix was the most popularly used algorithm.Quickbird was the widely used sensor. The use of machine-learning classifiers, particularly, support vector machine and random forest yielded, comparatively, high accuracies (>80%). Interestingly, deep learning showed potential to advance IS identification. Multi-city comparison studies demonstrated need for texture features to be locally specific in order to allow transferability. Thus, integration of RS data and field survey statistics could be crucial in enhancing understanding of morphological variations for improved IS mapping. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. AN AUTOMATED APPROACH TO EXTRACTING RIVER BANK LOCATIONS FROM AERIAL IMAGERY USING IMAGE TEXTURE.
- Author
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McKay, P. and Blain, C. A.
- Subjects
REMOTE sensing ,RIVER ecology ,SHANNON & Weaver's model (Communication) ,ENTROPY ,LAKESHORE development - Abstract
ABSTRACT A fundamental challenge in river analysis and modelling is the lack of readily available and reliable information on river bank geometry. Traditional survey methods are expensive and time consuming and often difficult to execute in many river systems because of hazardous terrain or lack of access. However, as high quality aerial and satellite imagery becomes available for more of the globe, it is increasingly possible to extract these bank locations directly from imagery. The most direct method of doing this involves manually designating edges based on visual criterion. This, however, is often prohibitively time consuming and labour intensive, and the quality is dependent on the individual doing the task. This paper describes a quick and fully automated method for locating water surface and river banks in high resolution aerial imagery without recourse to any multispectral information, by segmenting based on the local entropy of the image. This method is demonstrated on imagery of several rivers and its advantages and limitations are discussed. Published 2013. This article is a U.S. Government work and is in the public domain in the USA. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
43. High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction
- Author
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Wenna Xu, Yuan Shen, Jinsong Chen, Yingfei Xiong, Luyi Sun, Xiaorou Zheng, Shanxin Guo, Xinping Deng, and Xiaoqin Wang
- Subjects
Computer science ,0211 other engineering and technologies ,02 engineering and technology ,Land cover ,lcsh:Chemical technology ,Biochemistry ,Article ,Analytical Chemistry ,remote sensing ,Image texture ,Robustness (computer science) ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,021101 geological & geomatics engineering ,business.industry ,Deep learning ,cultivated land extraction ,deep learning ,Pattern recognition ,Vegetation ,U-Net ,Atomic and Molecular Physics, and Optics ,Random forest ,Support vector machine ,full convolutional network ,Kernel (image processing) ,Agriculture ,Thematic Mapper ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
Accurate and efficient extraction of cultivated land data is of great significance for agricultural resource monitoring and national food security. Deep-learning-based classification of remote-sensing images overcomes the two difficulties of traditional learning methods (e.g., support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF)) when extracting the cultivated land: (1) the limited performance when extracting the same land-cover type with the high intra-class spectral variation, such as cultivated land with both vegetation and non-vegetation cover, and (2) the limited generalization ability for handling a large dataset to apply the model to different locations. However, the &ldquo, pooling&rdquo, process in most deep convolutional networks, which attempts to enlarge the sensing field of the kernel by involving the upscale process, leads to significant detail loss in the output, including the edges, gradients, and image texture details. To solve this problem, in this study we proposed a new end-to-end extraction algorithm, a high-resolution U-Net (HRU-Net), to preserve the image details by improving the skip connection structure and the loss function of the original U-Net. The proposed HRU-Net was tested in Xinjiang Province, China to extract the cultivated land from Landsat Thematic Mapper (TM) images. The result showed that the HRU-Net achieved better performance (Acc: 92.81%, kappa: 0.81, F1-score: 0.90) than the U-Net++ (Acc: 91.74%, kappa: 0.79, F1-score: 0.89), the original U-Net (Acc: 89.83%, kappa: 0.74, F1-score: 0.86), and the Random Forest model (Acc: 76.13%, kappa: 0.48, F1-score: 0.69). The robustness of the proposed model for the intra-class spectral variation and the accuracy of the edge details were also compared, and this showed that the HRU-Net obtained more accurate edge details and had less influence from the intra-class spectral variation. The model proposed in this study can be further applied to other land cover types that have more spectral diversity and require more details of extraction.
- Published
- 2020
44. Frequency Spectrum-Based Optimal Texture Window Size Selection for High Spatial Resolution Remote Sensing Image Analysis
- Author
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Ju Fang, Lin Liu, Lu Xu, Weizhi Ma, Dongping Ming, Min Cao, and Xiao Ling
- Subjects
010504 meteorology & atmospheric sciences ,Scale (ratio) ,Article Subject ,Computer science ,0211 other engineering and technologies ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image processing ,02 engineering and technology ,01 natural sciences ,Texture (geology) ,Analytical Chemistry ,symbols.namesake ,Image texture ,lcsh:QC350-467 ,Image resolution ,Spectroscopy ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,Atomic and Molecular Physics, and Optics ,Fourier transform ,Frequency domain ,Computer Science::Computer Vision and Pattern Recognition ,symbols ,lcsh:Optics. Light ,Energy (signal processing) - Abstract
Image texture is an important visual cue in image processing and analysis. Texture feature expression is an important task of geo-objects expression by using a high spatial resolution remote sensing image. Texture features based on gray level co-occurrence matrix (GLCM) are widely used in image spatial analysis where the spatial scale is especially of great significance. Based on the Fourier frequency-spectral analysis, this paper proposes an optimal scale selection method for GLCM. Different subset textures are firstly upscaled by GLCM with different window sizes. Then the multiscale texture feature images are converted into the frequency domain by Fourier transform. Consequently, the radial distribution and angular distribution curves changing with different window sizes from spectrum energy can be achieved, by which the texture window size can be selected. In order to verify the validity of this proposed texture scale selection method, this paper uses high-resolution fusion images to classify land cover based on multiscale texture expression. The results show that the proposed method combining frequency-spectral analysis-based texture scale selection can guarantee the quality and accuracy of the classification, which further proves the effectiveness of optimal texture window size selection method bases on frequency spectrum analysis. Other than scale selection in spatial domain, this paper casts a novel idea for texture scale selection in the frequency domain, which is meant for scale processing of remote sensing image.
- Published
- 2019
- Full Text
- View/download PDF
45. An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites
- Author
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Lu Xiaotian, Yang Xue, Lu Ming, Li Feng, Zhang Nan, and Lei Xin
- Subjects
010504 meteorology & atmospheric sciences ,Image quality ,Computer science ,Science ,Multispectral image ,0211 other engineering and technologies ,Bilinear interpolation ,super-resolution ,02 engineering and technology ,01 natural sciences ,remote sensing ,Image texture ,mapping ,total variation ,resolution enhancement ,Projection (set theory) ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Contextual image classification ,Pixel ,business.industry ,Pattern recognition ,Superresolution ,Geostationary orbit ,General Earth and Planetary Sciences ,Artificial intelligence ,business - Abstract
Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.
- Published
- 2020
- Full Text
- View/download PDF
46. Assessing the Trade-Offs of SPOT7 Imagery for Monitoring Natural Forest Canopy Intactness
- Author
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Syd Ramdhani, Sershen, Astika Bhugeloo, and Kabir Peerbhay
- Subjects
Canopy ,Synthetic aperture radar ,010504 meteorology & atmospheric sciences ,Natural forest ,Multispectral image ,0211 other engineering and technologies ,Forestry ,02 engineering and technology ,lcsh:QK900-989 ,multispectral imagery ,01 natural sciences ,Normalized Difference Vegetation Index ,Random forest ,Image texture ,Forest ecology ,variable importance ,lcsh:Plant ecology ,Environmental science ,random forest ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,sub-tropical forests ,synthetic aperture radar - Abstract
Natural and human-induced disturbances influence the biodiversity and functionality of forest ecosystems. Regular, repeated assessments of canopy intactness are essential to map site-specific forest disturbance and recovery patterns, an essential requirement for forest monitoring and management. However, accessibility to images required for this practice, uncertainty around the levels of accuracy achieved with images of different resolution, and the affordability of the practice challenges its application in many developing regions. This study aimed to compare the accuracy of forest gap detection (in subtropical forests) achieved with lower-resolution (SPOT7 5 m) and higher-resolution (SPOT7 1.5 m) pan-sharpened imagery. Additionally, the Normalised Difference Vegetation Index (NDVI) and Synthetic Aperture Radar (SAR) were compared in terms of their ability to increase the accuracy of this detection when used in conjunction with both high and low resolution imagery. Results indicate that the SPOT7 1.5 m imagery produced an overall accuracy of 77.78% and a ϰ coefficient of 0.66 compared with the 69.44% accuracy and the 0.59 ϰ coefficient achieved with the SPOT7 5 m imagery. Computing image texture analysis within the Random Forest classifier (RF) framework increased classification accuracies to 75.00% for the SPOT 5 m and 86.11% for the SPOT7 1.5 m imagery, validating the usefulness of texture analysis. Variable importance was used to identify wavebands and texture-derived variables that were the most effective in discriminating canopy gaps from intact canopy. In this regard, near infrared, NDVI, SAR, contrast, mean, entropy and second moment were the most important. Collectively the results indicate that the approach adopted in this study, i.e., the use of SPOT7 1.5 m imagery in conjunction with image texture analysis and variable importance, can be used to accurately discriminate between canopy gaps and intact canopy, making it a cost-effective spatial approach for monitoring and managing natural forests.
- Published
- 2018
- Full Text
- View/download PDF
47. Transferability of vegetation recovery models based on remote sensing across different fire regimes.
- Author
-
Fernández‐Guisuraga, José Manuel, Suárez‐Seoane, Susana, Calvo, Leonor, and Feilhauer, Hannes
- Subjects
- *
WILDFIRES , *REMOTE sensing , *STANDARD deviations , *RIPARIAN plants , *GROUND vegetation cover , *REMOTE-sensing images , *DEAD trees - Abstract
Aim: To evaluate the transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data at different spatial resolutions within two Mediterranean pine forest sites affected by large wildfires in 2012. Location: The northwest and east of the Iberian Peninsula. Methods: In each study site, we defined three fire recurrence scenarios for a reference period of 35 years. We used image texture derived from the surface reflectance channels of WorldView‐2 and Sentinel‐2 (at a spatial resolution of 2 m × 2 m and 20 m × 20 m, respectively) as predictors of post‐fire vegetation cover in Random Forest regression analysies. Percentage vegetation cover was sampled in two sets of field plots with a size roughly equivalent to the spatial resolution of the imagery. The plots were distributed following a stratified design according to fire recurrence scenarios. Model transferability was assessed within each study site by applying the vegetation cover model developed for a given fire recurrence scenario to predict vegetation cover in other scenarios, iteratively. Results: For both wildfires, the highest model transferability between fire recurrence scenarios was achieved for those holding the most similar vegetation community composition regarding the balance of species abundance according to their plant‐regenerative traits (root mean square error [RMSE] around or lower than 15%). Model transferability performance was highly improved by fine‐grained remote‐sensing data. Conclusions: Fire recurrence is a major driver of community structure and composition so the framework proposed in this study would allow land managers to reduce efforts in the context of post‐fire decision‐making to assess vegetation recovery within large burned landscapes with fire regime variability. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
48. Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency.
- Author
-
Zhang, Jingcheng, Hu, Yuncai, Li, Fei, Fue, Kadeghe G., and Yu, Kang
- Subjects
REMOTE sensing ,SUSTAINABLE agriculture ,SIGNAL processing ,ENERGY crops ,AGRICULTURAL productivity ,TECHNOLOGY assessment ,AGRICULTURAL forecasts ,DRONE aircraft - Abstract
Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health across multiple spectral domains. However, this technology's precision in estimating plant traits associated with Nitrogen Use Efficiency (NUE), and the factors affecting this precision, are not well-documented. This review examines the capabilities of UASs in assessing NUE in crops. Our analysis specifically highlights how different growth stages critically influence NUE and biomass assessments in crops and reveals a significant impact of specific signal processing techniques and sensor types on the accuracy of remote sensing data. Optimized flight parameters and precise sensor calibration are underscored as key for ensuring the reliability and validity of collected data. Additionally, the review delves into how different canopy structures, like planophile and erect leaf orientations, uniquely influence spectral data interpretation. The study also recognizes the untapped potential of image texture features in UAV-based remote sensing for detailed analysis of canopy micro-architecture. Overall, this research not only underscores the transformative impact of UAS technology on agricultural productivity and sustainability but also demonstrates its potential in providing more accurate and comprehensive insights for effective crop health and nutrient management strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping
- Author
-
Robert De Wulf, Frederick N. Numbisi, and Frieke Van Coillie
- Subjects
Synthetic aperture radar ,Agriculture and Food Sciences ,010504 meteorology & atmospheric sciences ,Cloud cover ,Geography, Planning and Development ,Multispectral image ,0211 other engineering and technologies ,mapping cocoa agroforests ,lcsh:G1-922 ,02 engineering and technology ,classification uncertainty ,CLASSIFICATION ACCURACY ,01 natural sciences ,GLCM textures ,Image texture ,SYSTEMS ,random forest algorithm ,DRIVERS ,Earth and Planetary Sciences (miscellaneous) ,Computers in Earth Sciences ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing ,Mathematics ,Tropical deforestation ,grey level quantization ,VEGETATION INDEXES ,Congo Basin rainforest ,FOREST ,FRAMEWORK ,COVER ,Random forest ,machine learning ,Earth and Environmental Sciences ,SAMPLE-SIZE ,Grey level ,Sentinel-1 ,DEFORESTATION ,CARBON STOCKS ,Cropping ,lcsh:Geography (General) ,SAR - Abstract
Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification&rsquo, s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.
- Published
- 2019
50. An improved generative adversarial network for remote sensing image super‐resolution.
- Author
-
Guo, Jifeng, Lv, Feicai, Shen, Jiayou, Liu, Jing, and Wang, Mingzhi
- Subjects
GENERATIVE adversarial networks ,DEEP learning ,HIGH resolution imaging ,REMOTE sensing ,SIGNAL-to-noise ratio ,SPATIAL resolution - Abstract
Spatial resolution is an important indicator that measures the quality of remote sensing images. Image texture has been successfully recovered by generative adversarial networks in deep learning super‐resolution (SR) methods. However, the existing methods are prone to image texture distortion. To solve the above problems, this paper proposes an improved generative adversarial network to enhance the super‐resolution reconstruction effect of medium‐ and low‐resolution (LR) remote sensing images. This network is based on the Super Resolution Generative Adversarial Network (SRGAN), which makes great improvements in the structure of the connection between the inside and outside of the residual block and the design of the model loss function. At the same time, the G1–G2–G3 structure between residuals effectively combines the image information of the three scales of small, medium and large. The model loss function can be designed based on the Charbonnier loss function to narrow the pixel distance between the reconstructed remote sensing image and the original image. Furthermore, targeted perceptual loss can direct the network to restore the texture details of the image according to the semantic category. The subjective and objective evaluation of the generated images and the ablation experiments prove that compared with SRGAN and other networks, our method can generate more realistic and reliable textures. Additionally, the indicators [peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), multiscale structural similarity (MS‐SSIM)] used to measure the quality of the reconstructed image obtain improved objective quantitative evaluation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
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