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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]

Image texture is a complex visual perception. With the ever-increasing spatial resolution of remotely sensed data, the role of image texture in image classification has increased. Current approaches to image texture analysis rely on a single band of spatial information to characterize texture. This paper presents a multiscale approach to image texture where first and second-order statistical measures were derived from different sizes of processing windows and were used as additional information in a supervised classification. By using several bands of textural information processed with different window sizes (from 5×5 to 15×15) the main forest stands in the image were improved up to a maximum of 40%. A geostatistical analysis indicated that there was no single window size that would adequately characterize the range of textural conditions present in this image. A number of different statistical texture measures were compared for this image. While all of the different texture measures provided a degree of improvement (from 4 to 13% overall), the multiscale approach achieved a higher degree of classification accuracy regardless of which statistical procedure was used. When compared with single band texture measures, the level of overall improvement varied between 4 and 8%. The results indicate that this multiscale approach is an improvement over the current single band approach to analysing image texture. [ABSTRACT FROM AUTHOR]

The extraction of river planforms from remotely sensed satellite images is a task of crucial importance to many applications such as land planning, water resource monitoring, or flood prediction. In this article, we present a novel framework for the extraction of rivers from synthetic aperture radar (SAR) images, based on superpixel segmentation and subsequent classification. Superpixel segmentation is achieved by a modeling of the image pixels’ amplitudes and spatial coordinates as a finite mixture model, where the generalized Gamma distribution is used to model accurately a variety of high-resolution SAR scenes. A number of features describing image texture and statistics are extracted on a superpixel level, facilitating the identification of river superpixels—planforms are then extracted by unsupervised, agglomerative clustering, thus eliminating the need for labeled training data. We present the results of our proposed method on the ICEYE-X2 and SENTINEL-1 SAR data, demonstrating its ability to produce pixel-accurate river masks. [ABSTRACT FROM AUTHOR]

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User's accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. [ABSTRACT FROM AUTHOR]

Mapping hazelnut orchards can facilitate land planning and utilization policies, supporting the development of cooperative precision farming systems. The present work faces the detection of hazelnut crops using optical and radar remote sensing data. A comparative study of Machine Learning techniques is presented. The system proposed utilizes multi-temporal data from the Sentinel-1 and Sentinel-2 datasets extracted over several years and processed with cloud tools. We provide a dataset of 62,982 labeled samples, with 16,561 samples belonging to the ‘hazelnut’ class and 46,421 samples belonging to the ‘other’ class, collected in 8 heterogeneous geographical areas of the Viterbo province. Two different comparative tests are conducted: firstly, we use a Nested 5-Fold Cross-Validation methodology to train, optimize, and compare different Machine Learning algorithms on a single area. In a second experiment, the algorithms were trained on one area and tested on the remaining seven geographical areas. The developed study demonstrates how AI analysis applied to Sentinel-1 and Sentinel-2 data is a valid technology for hazelnut mapping. From the results, it emerges that Random Forest is the classifier with the highest generalizability, achieving the best performance in the second test with an accuracy of 96% and an F1 score of 91% for the ‘hazelnut’ class. [ABSTRACT FROM AUTHOR]

Both artificial visual interpretation and computer automatic classification were the mainly remote sensing methods used to extract farmland information. At present, it is still difficult to entirely replace the artificial visual interpretation for the computer automatic classification to extract farmlands' type information of the remote sensing image, because the automatic method needs more efforts to improve the precision of the classification results, so the problem became the key link of the automatic classification extraction. How to extract farmlands' type information in the western part of Jilin is one of the major problems which the paper attempts to solve to distinguish paddy field from dry land. A new solution to extract the farmland information has been designed for the remote sensing automatic classification, based on the spatial variation theory. The classification scheme was carried out by operating in an R language platform and the remote sensing software ERDAS platform. The farmland type of Zhenlai in the western of Jilin was extracted and monitored by making use of four indexes, the range of NDVI series, the local variance of image texture, the modified soil adjusted vegetation index, and the normalized difference water index, which have significant meaning for the farmland cover type in the transition zone between the cropping area and the nomadic area. These variances with clear physical meaning information (including the vegetation, water, soil drought conditions) and phonological information were used to build a multi-dimensional feature space classification data set. The results indicated that: 1) the dry field of which an area of 1065.337 km2 was cultivated in the study area was the largest farmlands' type, and also was one of the most important ecological landscape types. It's the spatial distribution characteristic of the study area that is a relatively dispersed dry field, and a relatively concentrated paddy field; 2) based on the multidimensional space data set, the algorithm of a support vector machine (SVM) was chosen to automatically extract the farmland types' data of the paddy field and the dry land. The overall classification accuracy of the algorithm was 94%, the Kappa coefficient of the classification was 0.87, and the extracted accuracy of the paddy field was 98.3%, while the extracted accuracy of the dry land was 98%. The existing automatic extraction approach was implemented to obtain a comparatively ideal classification result; 3) through the farmland's regional analysis, a depression that has more lowland and easy seeper is suitable for the reclamation of paddy field. It's noted that the extracted classification has an obvious regional farmland type, and the regional features were consistent with the farming cultivation characteristics in the northeast plains; 4) both rice and corn were typical unimodal type growth crops, and the similar growth peak, but the result of the range (rice, 3.8±0.4; upland crops, 3.4±0.3;) noted that: the phenology information of different vegetation types has its own characteristics; this characteristic of the NDVI seasonal variation curve is real. [ABSTRACT FROM AUTHOR]

Benggang is a type of erosion landform that commonly occurs in the southern regions of China, posing significant threats to local farmland and human safety. Object-based classification (OBC) can be applied with high-resolution (HR) remote sensing images for detecting Benggang areas on a large spatial scale, offering essential data for aiding in the remediation efforts for these areas. Nevertheless, traditional image segmentation methods may face challenges in accurately delineating Benggang areas. Consequently, the extraction of spatial and textural features from these areas can be susceptible to inaccuracies, potentially compromising the detection accuracy of Benggang areas. To address this issue, this study proposed a novel approach that integrates Segment Anything Model (SAM) and OBC for Benggang detection. The SAM was used to segment HR remote sensing imagery to delineate the boundaries of Benggang areas. After that, the OBC was employed to identify Benggang areas based on spectral, geometrical, and textural features. In comparison to traditional pixel-based classification using the random forest classifier (RFC-PBC) and OBC based on the multi-resolution segmentation (MRS-OBC), the proposed SAM-OBC exhibited superior performance, achieving a detection accuracy of 85.46%, a false alarm rate of 2.19%, and an overall accuracy of 96.48%. The feature importance analysis conducted with random forests highlighted the GLDV Entropy, GLDV Angular Second Moment (ASM), and GLCM ASM as the most pivotal features for the identification of Benggang areas. Due to its inability to extract and utilize these textural features, the PBC yielded suboptimal results compared to both the SAM-OBC and MRS-OBC. In contrast to the MRS, the SAM demonstrated superior capabilities in the precise delineation of Benggang areas, ensuring the extraction of accurate textural and spatial features. As a result, the SAM-OBC significantly enhanced detection accuracy by 34.12% and reduced the false alarm rate by 2.06% compared to the MRS-OBC. The results indicate that the SAM-OBC performs well in Benggang detection, holding significant implications for the monitoring and remediation of Benggang areas. [ABSTRACT FROM AUTHOR]

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7. 8% for palustrine wetland/upland models and 17. 0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents. [Copyright &y& Elsevier]

As one of the world's major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km 2 , of which 1,127,294.42 km 2 of coniferous forests account for 52% of the total area, 981,690.98 km 2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km 2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development. [ABSTRACT FROM AUTHOR]

Lithological classification is a pivotal aspect in the field of geology, and traditional field surveys are inefficient and challenging in certain areas. Remote sensing technology offers advantages such as high efficiency and wide coverage, providing a solution to the aforementioned issues. The aim of this study is to apply remote sensing technology for lithological classification and attempt to enhance the accuracy of classification. Taking a study area in Jixi, Heilongjiang Province, China, as an example, lithological classification is conducted using high-resolution satellite remote sensing data from GF-2 and texture data based on gray-level co-occurrence matrix (GLCM). By comparing the accuracy of lithological classification using different methods, the support vector machine (SVM) method with the highest overall accuracy is selected for further investigation. Subsequently, this study compares the effects of combining GF-2 data with different texture data, and the results indicate that combining textures can effectively improve the accuracy of lithological classification. In particular, the combination of GF-2 and the Dissimilarity index performs the best among single-texture combinations, with an overall accuracy improvement of 7.8630% (increasing from 74.6681% to 82.5311%) compared to using only GF-2 data. In the multi-texture combination dataset, the Mean index is crucial for enhancing classification accuracy. Selecting appropriate textures for combination can effectively improve classification accuracy, but it is important to note that excessive overlaying of textures may lead to a decrease in accuracy. Furthermore, this study employs principal component analysis (PCA) and independent component analysis (ICA) to process the GF-2 data and combines the resulting PCA and ICA datasets with different texture data for lithological classification. The results demonstrate that combining PCA and ICA with texture data further enhances classification accuracy. In conclusion, this study demonstrates the application of remote sensing technology in lithological classification, with a focus on exploring the application value of different combinations of multispectral data, texture data, PCA data, and ICA data. These findings provide valuable insights for future research in this field. [ABSTRACT FROM AUTHOR]

Accurate determination of the spatial distribution of coastal wetlands is crucial for the management and conservation of ecosystems. Feature selection methods based on the Jeffries-Matusita (J-M) method include J-M distance with simple average ranking (JMave), J-M distance based on weights and correlations (JMimproved), and heuristic J-M distance (JMmc). However, as the impacts of these methods on wetland classification are different, their applicability has rarely been investigated. Based on the Google Earth Engine (GEE) and random forest (RF) classifier, this is a comparative analysis of the applicability of the JMave, JMimproved, and JMmc methods. The results show that the three methods compress feature dimensions and retain all feature types as much as possible. JMmc exhibits the most significant compression from a value of 35 to 15 (57.14%), which is 37.14% and 40% more compressed than JMave and JMimproved, respectively. Moreover, they produce comparable classification results, with an overall classification accuracy of 90.20 ± 0.19% and a Kappa coefficient of 88.80 ± 0.22%. However, different methods had their own advantages for the classification of different land classes. Specifically, JMave has a better classification only in cropland, while JMmc is advantageous for recognizing water bodies, tidal flats, and aquaculture. While JMimproved failed to retain vegetation and mangrove features, it enables a better depiction of the mangroves, salt pans, and vegetation classes. Both JMave and JMimproved rearrange features based on J-M distance, while JMmc places more emphasis on feature selection. As a result, there can be significant differences in feature subsets among these three methods. Therefore, the comparative analysis of these three methods further elucidates the importance of J-M distance in feature selection, demonstrating the significant potential of J-M distance-based feature selection methods in wetland classification. [ABSTRACT FROM AUTHOR]

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data. [ABSTRACT FROM AUTHOR]

Habitat maps at high thematic and spatial resolution and broad extents are fundamental tools for biodiversity conservation, the planning of ecological networks and the management of ecosystem services. To derive a habitat map for Switzerland, we used a composite methodology bringing together the best available spatial data and distribution models. The approach relies on the segmentation and classification of high spatial resolution (1 m) aerial imagery. Land cover data, as well as habitat and species distribution models built on Earth observation data from Sentinel 1 and 2, Landsat, Planetscope and LiDAR, inform the rule-based classification to habitats defined by the hierarchical Swiss Habitat Typology (TypoCH). A total of 84 habitats in 32 groups and 9 overarching classes are mapped in a spatially explicit manner across Switzerland. Validation and plausibility analysis with four independent datasets show that the mapping is broadly plausible, with good accuracy for most habitats, although with lower performance for fine-scale and linear habitats, habitats with restricted geographical distributions and those predominantly characterised by understorey species, especially forest habitats. The resulting map is a vector dataset available for interactive viewing and download from open EnviDat data sharing platform. The methodology is semi-automated to allow for updates over time. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing (HRRS) images have few spectra, low interclass separability and large intraclass differences, and there are some problems in land cover classification (LCC) of HRRS images that only rely on spectral information, such as misclassification of small objects and unclear boundaries. Here, we propose a deep learning fusion network that effectively utilizes NDVI, called the Dense-Spectral-Location-NDVI network (DSLN). In DSLN, we first extract spatial location information from NDVI data at the same time as remote sensing image data to enhance the boundary information. Then, the spectral features are put into the encoding-decoding structure to abstract the depth features and restore the spatial information. The NDVI fusion module is used to fuse the NDVI information and depth features to improve the separability of land cover information. Experiments on the GF-1 dataset show that the mean OA (mOA) and the mean value of the Kappa coefficient (mKappa) of the DSLN network model reach 0.8069 and 0.7161, respectively, which have good applicability to temporal and spatial distribution. The comparison of the forest area released by Xuancheng Forestry Bureau and the forest area in Xuancheng produced by the DSLN model shows that the former is consistent with the latter. In conclusion, the DSLN network model is effectively applied in practice and can provide more accurate land cover data for regional ESV analysis. [ABSTRACT FROM AUTHOR]

Urban forests maintain the ecological balance of cities and are significant in promoting the sustainable development of cities. Therefore, using advanced remote sensing technology to accurately extract forest green space in the city and monitor its change in real-time is very important. Taking Nanjing as the study area, this research extracted 55 vegetation phenological features from Sentinel-2A time series images and formed a feature set containing 81 parameters together with 26 features, including polarimetric- and texture-related information extracted from dual-polarization Sentinel-1A data. On the basis of the improved ABC (ABC-LIBSVM) feature selection method, the optimal feature subset was selected, and the forest coverage areas in the study area were accurately described. To verify the feasibility of the improved feature selection method and explore the potential for the development of multi-source time series remote sensing for urban forest feature extraction, this paper also used the random forest classification model to classify four different feature sets. The results revealed that the classification accuracy based on the feature set obtained by the ABC-LIBSVM algorithm was the highest, with an overall accuracy of 86.80% and a kappa coefficient of 0.8145. The producer accuracy and user accuracy of the urban forest were 93.21% and 82.45%, respectively. Furthermore, by combining the multi-source time series Sentinel-2A optical images with Sentinel-1A dual-polarization SAR images, urban forests can be distinguished from the perspective of phenology, and polarimetric- and texture-related features can contribute to the accurate identification of forests. [ABSTRACT FROM AUTHOR]

A major yet largely unsolved problem in the semantic classification of very high resolution remote sensing images is the design and selection of appropriate features. At a ground sampling distance below half a meter, fine-grained texture details of objects emerge and lead to a large intraclass variability while generally keeping the between-class variability at a low level. Usually, the user makes an educated guess on what features seem to appropriately capture characteristic object class patterns. Here, we propose to avoid manual feature selection and let a boosting classifier choose optimal features from a vast Randomized Quasi-Exhaustive (RQE) set of feature candidates directly during training. This RQE feature set consists of a multitude of very simple features that are computed efficiently via integral images inside a sliding window. This simple but comprehensive feature candidate set enables the boosting classifier to assemble the most discriminative textures at different scale levels to classify a small number of broad urban land-cover classes. We do an extensive evaluation on several data sets and compare performance against multiple feature extraction baselines in different color spaces. In addition, we verify experimentally if we gain any classification accuracy if moving from boosting stumps to trees. Cross-validation minimizes the possible bias caused by specific training/testing setups. It turns out that boosting in combination with the proposed RQE feature set outperforms all baseline features while still remaining computationally efficient. Particularly boosting trees (instead of stumps) captures class patterns so well that results suggest to completely leave feature selection to the classifier. [ABSTRACT FROM PUBLISHER]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. 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.)

Classification of remote sensing scene image (RSSI) has been broadly applied and has attracted increasing attention. However, scene classification methods based on convolutional neural networks (CNNs) require a large number of manually labeled samples as training data, which is time-consuming and costly. Therefore, generating labeled data becomes a practical approach. However, conventional scene generation based on generative adversarial networks (GANs) involve some significant limitations, such as distortion and limited size. To solve the mentioned problems, herein, we propose a method of RSSI generation using element geometric transformation and GAN-based texture synthesis. Firstly, we segment the RSSI, extracting the element information in the RSSI. Then, we perform geometric transformations on the elements and extract the texture information in them. After that, we use the GAN-based method to model and generate the texture. Finally, we fuse the transformed elements with the generated texture to obtain the generated RSSI. The geometric transformation increases the complexity of the scene. The GAN-based texture synthesis ensures the generated scene image is not distorted. Experimental results demonstrate that the RSSI generated by our method achieved a better visual effect than a GAN model. In addition, the performance of CNN classifiers was reduced by 0.44–3.41% on the enhanced data set, which is partly attributed to the complexity of the generated samples. The proposed method was able to generate diverse scene data with sufficient fidelity under conditions of small sample size and solve the accuracy saturation issues of the public scene data sets. [ABSTRACT FROM AUTHOR]

The problem of segmentation of natural and technogenic objects by panchromatic satellite images of ultra-high spatial resolution (<1 m) using texture analysis is considered. The effectiveness of various statistical methods for extracting textural features is analyzed. On the basis of numerical experiments, methods have been identified that make it possible to segment with high accuracy (>95%) the main types of natural and anthropogenic objects, as well as various structures of the plant canopy. The TTSPCA method is proposed, which makes it possible to share the most informative features extracted using various statistical methods. The results of test calculations show that this method has higher accuracy (>99%) in solving the problem of texture segmentation compared to the considered standard approaches. [ABSTRACT FROM AUTHOR]

Abstract: The classification of satellite imagery into land use/cover maps is a major challenge in the field of remote sensing. This research aimed at improving the classification accuracy while also revealing uncertain areas by employing a geocomputational approach. We computed numerous land use maps by considering both image texture and band ratio information in the classification procedure. For each land use class, those classifications with the highest class-accuracy were selected and combined into class-probability maps. By selecting the land use class with highest probability for each pixel, we created a hard classification. We stored the corresponding class probabilities in a separate map, indicating the spatial uncertainty in the hard classification. By combining the uncertainty map and the hard classification we created a probability-based land use map, containing spatial estimates of the uncertainty. The technique was tested for both ASTER and Landsat 5 satellite imagery of Gorizia, Italy, and resulted in a 34% and 31% increase, respectively, in the kappa coefficient of classification accuracy. We believe that geocomputational classification methods can be used generally to improve land use and land cover classification from imagery, and to help incorporate classification uncertainty into the resultant map themes. [Copyright &y& Elsevier]

Remote sensing image scene classification is a hot research area for its wide applications. More recently, fusion-based methods attract much attention since they are considered to be an useful way for scene feature representation. This paper explores the fusion-based method for remote sensing image scene classification from another viewpoint. First, it is categorized as front side fusion mode, middle side fusion mode, and back side fusion mode. For each fusion mode, the related methods are introduced and described. Then, classification performances of the single side fusion mode and hybrid side fusion mode (combinations of single side fusion) are evaluated. Comprehensive experiments on UC Merced, WHU-RS19, and NWPU-RESISC45 datasets give the comparison result among various fusion methods. The performance comparisons of various modes, and interactions among different fusion modes are also discussed. It is concluded that (1) fusion is an effective way to improve model performance, (2) back side fusion is the most powerful fusion mode, and (3) method with random crop+multiple backbone+average achieves the best performance. [ABSTRACT FROM AUTHOR]

Due to an increasing demand of animal husbandry in arid (semiarid) area in China, grassland monitoring based on big data has become a proliferating research focus in recent years. However, the spectral of grass is interfered by topographic relief or that of forest in remote sensing image classification, leading to confusing pixels. In this letter, Tangbula grassland in the middle section of the Tianshan Mountains in Xinjiang (China) was selected as the research area. Upon analysis, we developed a novel composite multifeature deep learning method of capsule network to realize rapid and high-precision remote sensing recognition of the mountainous grassland, through combining the spectral bands with all the extracted features [normalized difference vegetation index (NDVI), topographic, and texture]. Using the new method, the accuracy of the grassland and overall classifications reached the highest values of 91.60% and 96.13%, respectively, greater than those of 85.58% and 92.18%, respectively, of normal classifications without input from texture and topographic features. Compared with the other methods, the method we applied is better than support vector machine (SVM), random forest, and artificial neural network in terms of grassland extraction and classification accuracy. [ABSTRACT FROM AUTHOR]

In this article, the authors suggested a rotation-invariant local binary pattern-based weighted generalized closest neighbor (RILBP-WGCN) method for HSI classification. The proposed RILBP is an enhanced texture-based classification paradigm that utilizes local binary pattern filter for some designated bands to generate a broad sketch of spatial texture information. Likewise, the proposed WGCN technique efficiently maintains the spatial uniformity between the nearby pixels via utilizing a local weight scheme and point-to-set distance. Also, as a postprocessing step, a label enhancement method is included for additional enhancement of the label uniformity as well as increases the performance of classification method. The color composite remotely sensed image of the initial three subsequent bands is segmented into several consistent regions by utilizing the graph-based superpixel segmentation technique. Then, extracted super pixels have been made extra homogeneous by utilizing a segment grouping process. Finally, advanced decision-level fusion is also applied on the retrieved local LBP features and unique spectral features, where linear opinion pool executes a serious role for concatenating the probabilistic outcomes of numerous spectral as well as texture features. The authors evaluated the proposed technique by comparing them with the seven competing methods on numerous datasets related to HSI classification. Evaluation results confirmed that the classification effects of proposed RILBP-WGCN algorithm are significantly better in contrast to other competing classification schemes. [ABSTRACT FROM AUTHOR]

A ReliefF improved mRMR (RmRMR) criterion-based bag of visual words (BoVW) algorithm is proposed to filter the visual words that are generated with high information redundancy for remote sensing image classification. First, the contribution degree of each word to the classification is represented by its weighting parameter, which is assigned using the ReliefF algorithm. Next, the relevance and redundancy of each word are calculated according to the mRMR criterion with the addition of a dictionary balance coefficient. Finally, a novel dictionary discriminant function is established, and the globally discriminative small-scale dictionary subsets are filtered and obtained. Experimental results show that the proposed algorithm effectively reduces the amount of redundant information in the dictionary and better balances the relevance and redundancy of words to improve the feature descriptive power of dictionary subsets and markedly increase the classification precision on a high-resolution remote sensing image. [ABSTRACT FROM AUTHOR]

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The classification of hyperspectral images (HSIs) is one of the most popular topics in the remote sensing community. Numerous feature extraction methods have been proposed to improve the classification accuracy of HSIs. Recently, deep features extracted by convolution neural network (CNN) have been introduced into the classification process of HSIs. Due to the nonlinear and invariant advantages of the features, CNN methods provide a powerful tool for representing geographic objects and classifying HSIs. However, traditional deep features only extracted at pixel-level and often neglect multiscale characteristics of geographic objects. In this study, a new deep feature extraction method is proposed, which takes advantage of multi-scale object analysis and the CNN model. Firstly, multiscale image objects are obtained by the multiscale segmentation algorithm and multiscale low-level features of objects are extracted. Secondly, the CNN is devoted to obtain deep features from low-level object features at each scale, respectively. Thirdly, the obtained deep features at all scales are stacked and fed to one fully connected layer to extract the multiscale deep learning features for classification. Finally, the logistic regression classifier is applied to hyperspectral image (HSI) classification based on object-oriented multiscale deep features. The proposed method was carried out on three widely used hyperspectral data sets: University of Pavia, Salinas, and Washington DC. The results reveal that the proposed method provides better results than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. In a preliminary version of this work, we introduced two new high resolution satellite imagery datasets (SAT-4 and SAT-6) and proposed DeepSat framework for classification based on "handcrafted" features and a deep belief network (DBN). The present paper is an extended version, we present an end-to-end framework leveraging an improved architecture that augments a convolutional neural network (CNN) with handcrafted features (instead of using DBN-based architecture) for classification. Our framework, having access to fused spatial information obtained from handcrafted features as well as CNN feature maps, have achieved accuracies of 99.90\% and 99.84\% respectively, on SAT-4 and SAT-6, surpassing all the other state-of-the-art results. A statistical analysis based on Distribution Separability Criterion substantiates the robustness of our approach in learning better representations for satellite imagery. [ABSTRACT FROM AUTHOR]

The bag-of-words (BoW) model has been widely used for scene classification in recent state-of-the-art methods. However, inter-class similarity among scene categories and very high spatial resolution imagery makes its performance limited in the remote-sensing domain. Therefore, this research presents a new KAZE-based image descriptor that makes use of the BoW approach to substantially increase classification performance. Specifically, a novel multi-neighbourhood KAZE is proposed for small image patches. Secondly, the spatial pyramid matching and BoW representation can be adopted to use the extracted features and make an innovative BoW KAZE (BoWK) descriptor. Third, two bags of multi-neighbourhood KAZE features are selected in which each bag is regarded as separated feature descriptors. Next, canonical correlation analysis is introduced as a feature fusion strategy to further refine the BOWK features, which allows a more effective and robust fusion approach than the traditional feature fusion strategies. Experiments on three challenging remote-sensing data sets show that the proposed BoWK descriptor not only surpasses the conventional KAZE descriptor but also yields significantly higher classification performance than the state-of-the-art methods used now. Moreover, the proposed BoWK approach produces rich informative features to describe the scene images with low-computational cost and a much lower dimension. [ABSTRACT FROM AUTHOR]

Classification is an important technique for remote sensing data interpretation. In order to enhance the performance of a supervised classifier and ensure the lowest possible cost of the training samples used in the process, active learning (AL) can be used to optimize the training sample set. At the same time, integrating spatial information can help to enhance the separability between similar classes, which can in turn reduce the need for training samples in AL. To effectively integrate spatial information into the AL framework, this paper proposes a new active relearning (ARL) model for remote sensing image classification. In particular, our model is used to relearn the spatial features on the classification map, which contributes significantly to enhancing the performance of the classifier. We integrate the relearning model into the AL framework, with the aim to accelerate the convergence of AL and further reduce the labeling cost. Under the newly developed ARL framework, we propose two spatial–spectral uncertainty criteria to optimize the procedure for selecting new training samples. Furthermore, an adaptive multiwindow ARL model is also introduced in this paper. Our experiments with two hyperspectral images and two very high resolution images indicate that the ARL model exhibits faster convergence speed with fewer samples than traditional AL methods. Our results also suggest that the proposed spatial–spectral uncertainty criteria and the multiwindow version can further improve the performance when implementing ARL. [ABSTRACT FROM AUTHOR]

The sea surface vessel/ship classification is a challenging problem with enormous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary patterns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed to provide posterior-probability estimation. Experimental results on remote sensing ship image datasets demonstrate that the proposed approach shows a consistent improvement on performance when compared to some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Variation in the format and classification requirements for remote sensing data makes establishing a standard remote sensing sample dataset difficult. As a result, few remote sensing deep neural network models have been widely accepted. We propose a hybrid deep neural network model based on a convolutional auto-encoder and a complementary convolutional neural network to solve this problem. The convolutional auto-encoder supports feature extraction and data dimension reduction of remote sensing data. The extracted features are input into the convolutional neural network and subsequently classified. Experimental results show that in the proposed model, the classification accuracy increases from 0.916 to 0.944, compared to a traditional convolutional neural network model; furthermore, the number of training runs is reduced from 40,000 to 22,000, and the number of labelled samples can be reduced by more than half, all while ensuring a classification accuracy of no less than 0.9, which suggests the effectiveness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

High resolution remote sensing image contains abundant information, but remote sensing classification only based on spectral information is affected in the complex spectrum area. Crop area and other land-cover objects contain different texture features. This paper extracts texture information based on gray-level co-occurrence matrix and Gabor filters group, sets up spectrum-texture joint feature set. To enhance classification efficiency, Ensemble learning strategy is introduced to improve classical support vector machine and back propagation neural network classifiers in training process. To prove the effectiveness of proposed methods, several experiment images are utilized to execute experiments. Results indicate that proposed methods improve classification accuracy compared with classical algorithms significantly, and promote running efficiency compared with the situation of large sample, support corn area statistical process and yield estimation. [ABSTRACT FROM AUTHOR]

Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Consequently, the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image. IKONOS 1 m PAN images are classified using the proposed algorithm, and the classification accuracy is more than 88%. [ABSTRACT FROM AUTHOR]

Spatial correlation between pixels is important information for remotely sensed imagery classification. Data field method and spatial autocorrelation statistics have been utilized to describe and model spatial information of local pixels. The original data field method can represent the spatial interactions of neighbourhood pixels effectively. However, its focus on measuring the grey level change between the central pixel and the neighbourhood pixels results in exaggerating the contribution of the central pixel to the whole local window. Besides, Geary's C has also been proven to well characterise and qualify the spatial correlation between each pixel and its neighbourhood pixels. But the extracted object is badly delineated with the distracting salt-and-pepper effect of isolated misclassified pixels. To correct this defect, we introduce the data field method for filtering and noise limitation. Moreover, the original data field method is enhanced by considering each pixel in the window as the central pixel to compute statistical characteristics between it and its neighbourhood pixels. The last step employs a support vector machine (SVM) for the classification of multi-features (e.g. the spectral feature and spatial correlation feature). In order to validate the effectiveness of the developed method, experiments are conducted on different remotely sensed images containing multiple complex object classes inside. The results show that the developed method outperforms the traditional method in terms of classification accuracies. [ABSTRACT FROM AUTHOR]

In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum. [ABSTRACT FROM AUTHOR]

Automatic cloud detection from satellite imagery is a necessary preprocessing step in remote sensing. Given that humans can easily “see” clouds in an image because of salient region features, we adopt a visual attention technique in computer vision to automatically identify images with a significant cloud cover. The proposed method generates a rough cloud mask by using a top-down visual saliency model to qualitatively distinguish cloud images from noncloud images. First, an image is downsized for rapid processing. Some basic saliency maps of clouds are then generated by multilevel segmentation, the computation of cloud visual saliency features, and feature classification. Thereafter, we fuse the basic saliency maps by using a most-votes-win strategy to generate the cloud mask. With the cloud mask, a threshold is used to classify the images as cloud or noncloud images. A total of 200 RapidEye images are tested by using the algorithm. Of the cloud images, 92% are correctly identified. The average processing time is 1.8 s per image. [ABSTRACT FROM PUBLISHER]

Sugarcane identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Information on the sugarcane cultivation areas is of global economic and environmental significance. The study area is Guigang City located in the central area of Guangxi Province which is a good representation of the agricultural conditions. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity. The growing region of sugarcane in Guangxi Province is discrete, so the remote sensing spectral of sugarcane is vulnerable to be impacted on a variety of crops. There are certain difficulties in the use of multi-spectral remote sensing to extract sugarcane. Current techniques for mapping sugarcane are based mainly on MODIS satellite data and may not make full use of the texture characteristics. The objective of this research is to investigate the potential for the application of the China Environment Satellite HJ-1A/1B and Phenology in monitoring sugarcane cultivation areas in Guangxi province in southern China. In our approach, we explored several characteristics such as the time information, spectral characteristics and texture features, used an object-based image analysis method and decision tree method for mapping the sugarcane area over large areas based on multi-temporal China Environment Satellite HJ-1A/B Data. A CCD camera sensor with 30m spatial resolution on board the China Environment Satellite HJ-1A and B has both visible and near infrared bands and a revisit period of four days, thus the temporal Normalized Difference Vegetation Index (NDVI) can be obtained from HJ-1A and B data. A time series of the China Environment Satellite HJ-1A/B Data and DEM images was acquired in order to represent the wide range of pattern variation along the sugarcane crop cycle. Firstly, the phenology differences and between sugarcane and other crops, such as cassavas, rice, corn, in Guigang City were analyzed. Therefore the best recognition sessions of the sugarcane was proposed to be February, May, early August and December. Then, to derive the image objects, the multi-scale segmentation algorithm was used in Definiens Developer in which the classification rule set was established. The rule set consists of the Process Algorithm and Class Description which contain several membership functions of characteristic indexes. The useful characteristic indexes mapping the sugarcane from other crops are Spectral mean value, NDVI, Digital Evaluate Model (DEM) and Gray Level Co-occurrence Matrix (GLCM) homogeneity. Finally, the desired thematic map with sugarcane ready to harvest was generated. The mapping was then evaluated applying the confusion matrix method and Kappa statistics to the independent testing dataset which was composed of 102 field survey points and 48 point samples on Google Earth. The statistics indicated that the classification achieved an overall accuracy of 91.3% and a Kappa coefficient of 0.83. The results of this study show that the Object-Oriented method is very efficient for the sugarcane classification process to maximize elimination of other interfering factors and suggests that the China Environment Satellite HJ-1A/B has great potential in the development of an operational system for monitoring sugarcane growth in southern China. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) data are used extensively for terrain classification applying SAR features from various target decompositions and certain textural features. However, one source of information has so far been neglected from PolSAR classification: Color. It is a common practice to visualize PolSAR data by color coding methods and thus, it is possible to extract powerful color features from such pseudocolor images so as to provide additional data for a superior terrain classification. In this paper, we first review previous attempts for PolSAR classifications using various feature combinations and then we introduce and perform in-depth investigation of the application of color features over the Pauli color-coded images besides SAR and texture features. We run an extensive set of comparative evaluations using 24 different feature set combinations over three images of the Flevoland- and the San Francisco Bay region from the RADARSAT-2 and the AIRSAR systems operating in C- and L-bands, respectively. We then consider support vector machines and random forests classifier topologies to test and evaluate the role of color features over the classification performance. The classification results show that the additional color features introduce a new level of discrimination and provide noteworthy improvement in classification performance (compared with the traditionally employed PolSAR and texture features) within the application of land use and land cover classification. [ABSTRACT FROM PUBLISHER]

Accurate cotton maps are crucial for monitoring cotton growth and precision management. The paper proposed a county-scale cotton mapping method by using random forest (RF) feature selection algorithm and classifier based on selecting multi-features, including spectral, vegetation indices, and texture features. The contribution of texture features to cotton classification accuracy was also explored in addition to spectral features and vegetation index. In addition, the optimal classification time, feature importance, and the best classifier on the cotton extraction accuracy were evaluated. The results showed that the texture feature named the gray level co-occurrence matrix (GLCM) is effective for improving classification accuracy, ranking second in contribution among all studied spectral, VI, and texture features. Among the three classifiers, the RF showed higher accuracy and better stability than support vector machines (SVM) and artificial neural networks (ANN). The average overall accuracy (OA) of the classification combining multiple features was 93.36%, 7.33% higher than the average OA of the single-time spectrum, and 2.05% higher than the average OA of the multi-time spectrum. The classification accuracy after feature selection by RF can still reach 92.12%, showing high accuracy and efficiency. Combining multiple features and random forest methods may be a promising county-scale cotton classification method. [ABSTRACT FROM AUTHOR]