Search

Leaf Area Index (LAI) remains one of the most important forest structural attributes, as accurate estimation of LAI is crucial for predicting the growth of different species. Using the Partial Least Squares Regression (PLS-R) algorithm, this study investigated three image processing techniques to determine the best technique for estimating LAI using WorldView-3 imagery in the Midlands, KwaZulu-Natal province of South Africa. The PLS-R texture ratios model achieved the highest accuracy of R2 = 0.70, RMSE = 1.21 (2.32% of the mean measured LAI) and R2 = 0.72, RMSE = 1.26 (2.41% of the mean measured LAI) for the wet and dry seasons, respectively. This was followed by the PLS-R single texture band model that produced an accuracy of R2 = 0.65, RMSE = 1.35 (2.58% of the mean measured LAI) and R2 = 0.67, RMSE = 1.32 (2.52% of the mean measured LAI) for the wet and dry seasons, respectively. The PLS-R model using a combination of vegetation indices had the lowest estimation accuracy of R2 = 0.59, RMSE = 1.38 (2.64% of the mean measured LAI) and R2 = 0.60, RMSE = 1.40 (2.67% of the mean measured LAI) for the wet and dry seasons, respectively. The results of this study provided evidence that image texture ratios can be used to estimate LAI effectively. [ABSTRACT FROM AUTHOR]

The microtexture of the asphalt pavement, which is critical for the skid resistance, is dictated by the microtexture of the coarse aggregates used in the surface courses. The existing standard test methods can only capture the combined measure of coarse aggregate surface texture which can be influenced by other aggregate characteristics such as shape and angularity. Consequently, the surface texture of aggregates cannot be quantified explicitly using these tests, and therefore image analysis methods that can extract textural features are gaining more significance. In the present study, various image texture analysis methods are evaluated to identify a reliable texture indicator. Methods include traditional statistical approaches such as histogram method, Grey Level Co-occurrence Matrix (GLCM) methods, and transform-based wavelet texture analysis. The image analysis was carried out on the grey-scale scanning electron microscope (SEM) images before and after accelerated polishing. To compare the image texture parameters, the average surface roughness (Sa) measured from aggregate surface profile data obtained from a 3D Optical Surface Profilometer (3D OSP) was considered as a direct measure of surface texture. A detailed correlation analysis with Sa indicates that the surface texture index (STI) from the wavelet analysis quantifies the microtexture more accurately compared to statistical methods. [ABSTRACT FROM AUTHOR]

Background: The placental pathological changes in hypertensive disorders of pregnancy (HDP) starts early in pregnancy, the deep convolutional neural networks (CNN) can identify these changes before its clinical manifestation. Objective: To compare the placental quantitative ultrasound image texture of women with HDP to those with the normal outcome. Methods: The cases were enrolled in the first trimester of pregnancy, good quality images of the placenta were taken serially in the first, second, and third trimester of pregnancy. The women were followed till delivery, those with normal outcomes were controls, and those with HDP were cases. The images were processed and classified using validated deep learning tools. Results: Total of 429 cases were fully followed till delivery, 58 of them had HDP (13.5%). In the first trimester, there was a significant difference in the placental length (p = .033), uterine artery PI (p = .019), biomarkers PAPP-A (p = .001) PlGF (p = .013) and placental image texture (p = .001) between the cases and controls. In the second trimester the uterine artery PI, serum PAPPA (p = .010) and PlGF (p = .005) levels were significantly low among women who developed hypertension later on pregnancy. The image texture disparity between the two groups was highly significant (p < .001). The model "resnext 101_32x8d" had Cohen kappa score of 0.413 (moderate) and the accuracy score of 0.710 (good). In the first trimester the best sensitivity and specificity was observed for abnormal placental image texture (70.6% and 76.6%, respectively) followed by PlGF (64% and 50%, respectively), in the second trimester the abnormal image texture had the highest sensitivity and specificity (60.4% and 73.3%, respectively) followed by uterine artery PI (58.6% and 54.7%, respectively). Similarly in the third trimester, uterine artery PI had sensitivity and specificity of 60.3% and specificity of 50.7%, whereas the abnormal image texture had sensitivity and specificity of 83.5%. Conclusion: Ultrasound placental analysis using artificial intelligence (UPAAI) is a promising technique, would open avenues for more research in this field. [ABSTRACT FROM AUTHOR]

This paper proposes a novel distortion function for JPEG steganography, which depends on the magnitude of discrete cosine transformation (DCT) coefficients, the first- and second-order residuals, and the amount of zeros in a DCT block. The magnitude of a DCT coefficient indicates the ability of the current coefficient to conceal modification trace. The first- and second-order residuals make sufficient use of the correlation in DCT domain, and the amount of zeros in a DCT block illustrates the block texture well. These elements are combined together to measure the risks of detection due to the modification on the cover data. By using this proposed distortion function, the embedding changes are limited in texture regions when employing the syndrome trellis coding to embed secret data. Thus less detectable artefacts could be found in stego images. The experimental results demonstrate that the proposed scheme performs less statistical detectability when comparing to current state-of-the-art steganographic methods for JPEG images. [ABSTRACT FROM AUTHOR]

This article proposes an algorithm for script identification by textural analysis of the image corresponding to the script types. In the first phase, each letter is modeled by the equivalent script type, which is determined by its position in the baseline area. Then, feature extraction is carried out. It is based on the script type cooccurrence pattern analysis. The obtained features of the script are stored for further analysis. The difference in script characteristics contributes to the diversity of the extracted features, which simplify the feature classification obtained by an extension of a state-of-the-art classification tool called Genetic Algorithms Image Clustering for Document Analysis. Accordingly, it represents the key element in the decision-making process of script identification. The proposed method is tested on an example of German printed documents, which contain Latin and Fraktur scripts. The experiment shows correct results, which is promising. [ABSTRACT FROM PUBLISHER]

An accurate global carbon budget requires information on terrestrial carbon sink strength. Regenerating tropical forests are known to be important terrestrial carbon sinks but information on their location, extent and biomass (from which carbon content can be estimated) is incomplete. The use of remotely sensed data in optical wavelengths has been of limited use due to both the weak relationship between optical radiation and forest biomass and near‐constant cloud cover in the tropics. L‐band Synthetic Aperture Radar (SAR) backscatter, however, is related positively to biomass (but only up to an asymptote of around 40–90 T ha -1 ) and can be obtained independently of cloud cover. Both canopy structure and biomass change over time as pioneer species are replaced by early and late regenerating species. These structural changes are related to an increase in (i) tree height, (ii) tree species richness and (iii) canopy thickness and influence the roughness of the canopy surface and consequently SAR image texture. Therefore, we investigated the degree to which textural information could be used to increase the correlation between image tone (backscatter) and biomass. Field data were used to estimate the biomass of 37 regenerating forests plots in Brazilian Amazonia. Texture measures derived from local statistics, the grey level co‐occurrence matrix (GLCM) and the variogram were evaluated using simulated images on the basis of their ability to identify significant differences in image texture independently of image contrast. The selected texture measures were applied to L‐band JERS‐1 (Japanese Earth Resources Satellite) SAR images and the correlation between backscatter and biomass was determined for regenerating tropical forests. A strong correlation was found for the texture measures and biomass. The r a 2 (adjusted coefficient of determination), measuring the correlation between backscatter and biomass, increased from 0.74 to 0.82 with the addition of GLCM‐derived contrast. The addition of image texture (GLCM‐derived contrast) to image tone (backscatter) potentially increases the accuracy with which JERS‐1 SAR data can be used to estimate biomass in tropical forests. [ABSTRACT FROM AUTHOR]

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]

A Compact Airborne Spectrographic Imager (CASI) multiresolution dataset, comprised of imagery with spatial resolutions of 60 cm, 1 m and 2 m, was used to asses the relationship between defoliation severity of aspen ( Populus tremuloides Michx.) stands infested with the Bruce spanworm ( Operophtera bruceata ), the Leaf Area Index (LAI) of these stands and the CASI image components comprising of Normalized Difference Vegetation Index (NDVI) and image texture. The study area was located in the foothills of the Alberta Canadian Rockies. Defoliation severity, LAI, and crown closure of the stands were measured on the ground. Multiple stepwise regression methods were used to develop relationships between the field and imagery data. Image texture derived from the grey-level co-occurrence matrix of the first principle component, or 'brightness' image, was incorporated into the discriminant analysis of defoliation severity classes. The highest spatial resolution imagery outperformed the coarser image resolutions. The characteristics of the defoliation changed the spectral response of the moderately and severely defoliated stands considerably when compared to healthy stands. The following paper demonstrates that aspen defoliation severity can be detected with CASI image data, specifically through the incorporation of imagery spatial component captured by image texture. [ABSTRACT FROM AUTHOR]

The suitability of optical IKONOS satellite data (multispectral and panchromatic) for the estimation of forest structural attributes – for example, stems per hectare (SPHA), diameter at breast height (DBH), mean tree height (MTH), basal area (BA) and volume in plantation forest environments – was assessed in this study. The relationships of these forest structural attributes to statistical image texture from IKONOS imagery were analysed. The coefficients of determination (R 2) of multilinear regression models developed for the estimation of SPHA, DBH, MTH, BA and volume using statistical texture features from multispectral data were 0.63, 0.68, 0.81, 0.86 and 0.86, respectively. When the statistical texture features from panchromatic data were applied, the R 2 for the respective forest structural attributes increased by 25%, 31%, 6%, 0.2% and 0.2%, respectively. Artificial neural network (ANN) models produced strong and significant relationships between estimated and actual measures of SPHA, DBH, MTH, BA and volume with an R 2 of 0.83, 0.83, 0.90, 0.90 and 0.92, respectively, based on multispectral IKONOS data. Based on panchromatic IKONOS imagery, the R 2 for the respective forest structural attributes increased by 18%, 12%, 5%, 3% and 6%, respectively. Results such as these bode well for the application of high spatial resolution imagery to forest structural assessment. [ABSTRACT FROM AUTHOR]

This study evaluated image texture within a multi-scale context. The multi-scale texture profile is a representation of texture as a function of scale. It is related to the variogram, but has the advantage of being a direct measure of local texture over a specified kernel. Directional texture is a method to capture the dependence of texture on orientation using 1-dimensional kernels, and is potentially useful for quantifying texture for classes with a linear shape, such as roads or rivers. Seven derived texture attributes were proposed from the multi-scale texture profile and directional texture: the minimum, median, mean, maximum, range, minimum directional, and minimum multi-scale and directional texture. The derived texture attributes were found to be useful in developing a locally adaptive texture measure, which uses different texture attributes for different locations in the image. In order to develop the rules for the adaptive method, seven characteristic multi-scale texture profiles were identified. Rules were established to identify the general classes, and each pixel was assigned to one class only, depending on that pixel's texture profile. Optimal texture measures were proposed for each class. In an analysis using selected texture classes from IKONOS test data from Jeju, Korea, it was found that compared to all other texture measures, the adaptive texture provided the highest average and minimum classification accuracy, as well as the second highest maximum accuracy (69%, 51% and 92%, respectively). In comparison, the best fixed size kernel, 11 × 11 pixels, had accuracies in the same categories of 68%, 38% and 87%, and the original panchromatic image band had accuracies of 41%, 5% and 92%. Future work is needed to enhance the texture rules, and apply the adaptive texture method to other scales of data. [ABSTRACT FROM PUBLISHER]

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 land-use/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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Based on a differential box counting method and a gliding-box algorithm, a new method for estimating the lacunarity of grey scale digital image surfaces is introduced, and directionality of lacunarity defined. To test the performance of the new lacunarity measure, a Brodatz texture image mosaic is employed and several other texture analysis approaches are also applied to the texture mosaic. Quantitative comparison shows that the new lacunarity estimation method for grey-scale images can provide more accurate texture measurements than some existing lacunarity measures, the grey level co-occurrence matrix based texture measures, the Min-Max operator, and the fractal dimension. [ABSTRACT FROM AUTHOR]

In the past one difficulty of texture analysis was the lack of adequate tools to characterize different scales of texture effectively. Recent developments in multiresolution analysis such as the Gabor and wavelet transforms, help to overcome this difficulty. This paper introduces a new approach to characterize texture at multiple scales. The performances of the wavelet transform are measured in terms of sensitivity and selectivity for the classification of 25 types of remote sensing texture relief images under the condition of different wavelet decomposition models, different filter lengths, different resolutions and different mother bodies. The reliability exhibited by texture signatures of wavelet transforms are beneficial for accomplishing segmentation, classification and subtle discrimination of texture. [ABSTRACT FROM AUTHOR]

A Landsat TM image, part of a rainforest located in the state of Rondonia, Brazil, was analysed using Fourier transform techniques in order to extract textural features. The lattice-like texture pattern recorded was then compared with the geological structural trends of the area, and measures of the soil surface moisture treated by geostatistical analysis. The consistency between the different sets of analysis suggests that the spatial distribution of moisture at the soil surface is related to the structure of the bedrock. The near infrared channel of the Landsat TM may be used to record trends in water content distributions at the soil surface under homogeneous forest canopies. [ABSTRACT FROM AUTHOR]

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]

Flocs formation and growth are important characteristics in wastewater coagulation process. The shape and size of flocs highly affect further separation processes, therefore resulting treatment efficiency of wastewater after coagulation. Observed images of flocs tend to show strong relations to coagulation parameters: dose and coagulation time. In this article, three texture recognition techniques were evaluated for the ability to mathematically describe the relationship between the images of flocs and coagulant dosages. The easily computable texture analysis methods were found to be potential techniques for the characterization of the particles images. Ten out of eleven co-occurrence matrix-based grey level co-occurrence matrix (GLCM) texture features were found to be significant for the dosage prediction by a principal component regression model with only one principal component. Two features (Inverse difference moment and Variance) were selected for the multiple linear regression model. Test set prediction accuracy varied from 83 to 96% depending on texture analysis method and multivariate model. Best dosage prediction and image classification results were achieved by GLCM and angle measure technique. The results of image texture analysis coupled with multivariate modelling techniques indicate that it is possible to characterize and relate flocs images, captured during coagulation, with different coagulant dosages, as well as predict those dosages. [ABSTRACT FROM PUBLISHER]

Abstract This article proposes a noninvasive computer-aided assessment approach based on optimized convolutional neural network for healthy and pathological voice detection. Firstly, the input voice samples are first transformed into mel-spectrogram time-frequency visual representations and fed for training the CNN model. The time-frequency image captures inherent speech variations beneficial for healthy and pathological voice sample detection. The weights and biases of trained CNN network are further optimized using artificial bee colony (ABC) optimization algorithm resulting in optimum CNN network employed for testing unseen data. The proposed approach is evaluated using three popular and publicly available datasets: SVD, AVPD and VOICED. Experimental results emphasize that proposed ABC optimized CNN model shows improved accuracy performance by 1.02% compared to conventional CNN network illustrating data-independent discriminative representation ability. Finally, gradient-weighted class activation mapping (Grad-CAM) explainable artificial intelligence (XAI) is utilized to make the decision understandable. [ABSTRACT FROM AUTHOR]

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]

Visible watermarking is one of the most direct and effective copyright protection methods for digital images and videos, but it faces the problems of easy detection of watermarking location and watermarking removal attacks. It requires that the visible watermarking not only can not affect visual quality of original digital products but also can resist accidental editing and malicious attacks. In this paper, an adaptive region selection method for visible watermark embedding is proposed. First, an improved region selection method based on the model of saliency-based visual attention (MSVA) is used to more accurately distinguish salient and non-salient regions of host image; then, the non-salient regions of image are divided into sub-blocks to calculate texture complexity respectively; finally, a moderate image block is selected as the embedding region of visible watermark image according to the distribution of image texture complexity. The experimental results show that the proposed method can identify saliency regions more accurately, thus ensuring that the important contents of host image will not be occluded. The adaptive selection of embedding region based on image complexity not only ensures the recognizability and aesthetics of visible watermark embedding effect but also takes into account the security of watermark embedding, and especially reduces the risk of batch removal attacks. [ABSTRACT FROM AUTHOR]

An optimised design of pavement considering the noise reduction performance is desired to reduce the tyre/pavement noise. A cost-effective and user-friendly laboratory test method for measuring the tyre/pavement noise pressure spectrum is deemed necessary towards an optimised design taking into account the noise reduction performance. In this paper, a novel laboratory tyre/pavement noise measurement method, namely tyre rolling-down (TR) method, is developed to quantify the tyre/pavement noise pressure spectrum. The analysis of noise generation mechanism on TR shows that it is capable of simulating the tyre/pavement noise generated through tyre vibration and aerodynamic effect. The tyre/pavement noise wave band is identified by conducting an experiment where different mixture samples are compacted in laboratory to generate a reasonable range of tyre/pavement noise levels. In addition, a 2-Dimensional Image Texture Analysis Method (2D-ITAM) is applied to measure surface texture properties of these specimens, which are input parameters of the empirical rolling noise prediction models. The total A-weighting tyre/pavement noise pressure levels from TR are compared and verified with those from empirical rolling noise prediction models based on pavement surface texture spectrum and acoustic absorption coefficient. It is shown that TR results correlate well with those calculated from the prediction models and could be considered as an effective method to characterise tyre/pavement noise in laboratory during the stage of asphalt pavement design. [ABSTRACT FROM AUTHOR]

The fractal dimension of a micrograph is a quantitative parameter of its geometric patterns' complexity, morphological irregularities and characteristics in their spatial arrangements and configurations. Material defects growing during fatigue process are described in terms of f r a c t a l s . The experimental assessment of fractal dimensions of the novel flaky electron fractographs comprising the 3D planar topography of branched secondary cracks with distinctively oriented striations' i s l a n d s and tearing ridges' n e t w o r k s on the 'fatigue crack propagation area' in three different Zr–Nb alloys have been carried out and correlated with their corresponding low cycle fatigue responses at different temperatures. Image texture analysis has also been implemented to understand the relative 'energy generation' of the hierarchical fatigue crack propagation during f a t i g u e − f r a c t u r e process of these materials as a function of test temperature. The detail invasive fractal character and chaos of these crack/striations' i s l a n d s and tearing ridge n e t w o r k morphologies after f a t i g u e − f r a c t u r e have been convincingly revealed. [ABSTRACT FROM AUTHOR]

Pavement skid resistance plays a key role in traffic safety. Meanwhile, tire-pavement noise is a major source of traffic noise in urban areas. Current asphalt mixture design methods, however, mostly focus on volumetric and mechanical properties and pay little attention to the skid resistance and noise reduction performance of asphalt mixtures, which are significantly affected by the surface textures of asphalt mixtures. Incorporating the evaluation of surface texture into the mixture design would aid in a more rational selection of materials considering both mechanical and functional properties of asphalt mixtures. In this paper, the surface texture properties of several types of asphalt mixtures are measured using a recently developed 2-Dimensional Image Texture Analysis Method. A prediction model correlating the mixture surface texture levels at different central texture wavelength in octave band with the important mix design parameters is established using a multivariate non-linear regression analysis. The model is validated through laboratory test and imaging measurement indicating its capability of predicting the level and distributions of mixture surface texture. The prediction model is anticipated to provide a basis of optimised mixture design considering the skid resistance and noise reduction performances of asphalt pavement. [ABSTRACT FROM AUTHOR]

Inspection robots for early detection of potential risks in severe environments require a high accuracy like human experts. A fine mechanism is crucial for extracting target components from noisy signals. We have proposed a detection system for submillimeter-width cracks in concrete surfaces of social infrastructures, such as bridges, by using morphological component analysis (MCA), for aerial image-inspection robots. Traditional schemes like PCA have relied on linear decomposition for the separation of target signal and noise components. Recent advancement in signal decomposition focuses on the enhancement of linearity in the separation by introducing a set of nonlinear basis functions to represent the raw signal even when multiple factors are mixed in a nonlinear manner. In this sense, MCA is a core technique to be able to isolate target components to represent submillimeter-width cracks from others. We proposed a proper pre- and post-processing operations to attach MCA, which demonstrated a high accuracy yet coarse and fine image components have to be integrated redundantly. In the present study, we successfully found a simpler mechanism to set the single basis function to extract the target by introducing a new thresholding mechanism. It suggests a high potential of MCA for inspection robots for various purposes. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology and the development of internet sources, e-learning draws huge attention as it is capable of providing classroom lectures in the form of videos. Here, a method has been proposed for effective retrieval of the lecture videos, which employs the correlated naive Bayes (CNB) classifier. In this proposed method, Optical Character Recognition uses the tesseract classifier and GOM (Gabor Ordinal Measure) to extract the textual features and the image texture from the key frames. K-means clustering clusters the features and the classifier retrieves the relevant video. Experimentation has been done in the MATLAB and the parameters such as precision, recall, and F measure of the CNB are compared over the other methods such as K-Nearest Neighbour and naive Bayes (NB) classifiers. The CNB classifier achieves a better precision, recall, and F measure rate of 0.9366, 0.9511, and 0.9426, respectively. [ABSTRACT FROM AUTHOR]

Osmotic dehydration (OD) and microwave drying (MD) have been applied to fruits to preserve their functional and quality attributes. The aim of this study was to determine the correlation between different parameters (shrinkage, color, image texture, microstructure) on the quality of three different types of cuts (oval, longitudinal, transversal) of mango slices when sequentially dried by OD, MD, and microwave oven-processing (MOP). After each processing, the samples showed different changes in geometrical parameters, shrinkage, color, image texture, and microstructure from inside the cell structure to the macroscopic level. Principal components analysis (PCA) was used to select the most suitable cut type with the least separation distance between fresh and processed samples. The most suitable cut after the processing was the longitudinal cut which was associated with higher quality mango slices. This contribution provides an insight into how the cut types affect the drying, shrinkage, color parameters, and microstructure when studying the quality of dried fruits by means of multivariate analysis. [ABSTRACT FROM AUTHOR]

Automatic detection of corrosion inhibition efficacy by processing the microscopic images of the sample surface by using image texture analysis is a very useful technique in the contemporary research field. The reported technique is less time consuming with the capability of pattern recognition via quantification with preferences of suitable software, coding and various numerical methods ultimately represents data (vide acquisition) more precisely and accurately at agiven condition. An optimal anisotropic diffusion-based pre-processing for image enhancement and run length statistics (RLS) based texture analysis for feature extraction are performed on the microscopic images of mild steel surface treated in 1 M HCl solution with different inhibitors viz., (E)-1-(2,4-dinitrophenyl)-2-(pyridin-4-yl)methylene)hydrazine (abbreviated as DHMP and represented as L1), (E)-1-(4-isocyanophenyl)-2-((pyridin-4-yl)methylene)hydrazine) (abbreviated as IMH and represented as L2) and (E)-1-(2,4-dinitrophenyl)-2-((pyridin-4-yl)methylene)phenol (abbreviated as DMP and represented as L3). Extracted normalized or relative features can be quantified the corrosion inhibition efficiency in an excellent way using multi-regression analysis having R-square values in the range of 0.997 to 0.999. (E)-1-(2,4-dinitrophenyl)-2-(pyridin-4-yl)methylene)hydrazine (abbreviated as DHMP and represented as L1), (E)-1-(4-isocyanophenyl)-2-((pyridin-4-yl)methylene)hydrazine) (abbreviated as IMH and represented as L2) and (E)-1-(2,4-dinitrophenyl)-2-((pyridin-4-yl)methylene)phenol (abbreviated as DMP and represented as L3) are used as three different corrosion inhibitors over the mild steel surface in presence of 1M HCl solution. Automatic detection of corrosion inhibition efficiency by processing the microscopic images of the targeted sample surface by via image texture analysis. An optimal anisotropic diffusion technique is adopted in this present study for image enhancement before processing the image. Run length statistics (RLS) based texture analysis has been incorporated in this present investigation for extracting run length features to detect the corrosion inhibition efficiency. After selecting the RLS-based features, a multivariable regression model is developed and correlations are set up with the features of conventional parameters of the different inhibitor's performance. [ABSTRACT FROM AUTHOR]

Commercial forest species discrimination is valuable for optimal management of commercial forests. Therefore, second-order image texture combinations computed from a 0.5 m WorldView-2 pan-sharpened image integrated with sparse partial least squares discriminant analysis (SPLS-DA) and partial least squares discriminant analysis (PLS-DA) were used to discriminate commercial forest species. The findings show that the SPLS-DA model, which is characterised by concurrent variable selection and reduction of data dimensionality, produced an overall classification accuracy of 86%, with an allocation disagreement of 9 and a quantity disagreement of 5. Conversely, the PLS-DA model with variable importance in projection (VIP) produced an overall classification accuracy of 81%, with an allocation disagreement of 12 and a quantity disagreement of 7. Overall, this study demonstrates the value of second-order image texture combinations in discriminating commercial forest species and presents an opportunity for improved commercial forest species delineation. [ABSTRACT FROM AUTHOR]

The pavement distress information is essential for evaluating the pavement. Automatic pavement inspection systems can be so beneficial in providing needed information on pavement conditions. The bleeding is one of the asphalt pavement distresses and directly affects the pavement skid resistance and road safety. This study aims to develop an image-based system for the comprehensive evaluation of pavement bleeding. This evaluation consists of three main parts: bleeding occurrence detection, bleeding region segmentation, and severity-based classification of bleeding regions. For implementing the proposed system, deep learning-based model and transfer learning method are used in the detection part, and wavelet transform is the main process in the segmentation part. Also, gray-level co-occurrence matrix, wavelet transform, and signal to noise ratio are used to extract image texture-based features from various levels of the bleeding severity (low, mid, and high). Then a decision tree has been made based on extracted features for severity-based classification. The proposed system can indicate the bleeding index based on distress density and severity. Results show good performance in detection, segmentation, and severity-based classification parts, on the average of performance indices with 98%, 89%, and 93%, respectively. Therefore, the proposed system provides an efficient method for comprehensive pavement bleeding evaluation. [ABSTRACT FROM AUTHOR]

Biospeckle activity (during 1-25 s) of ‘Red Delicious’ apples was recorded under 680 and 780 nm laser diodes. The time history speckle pattern (THSP) images were configured and evaluated using the common biospeckle and texture descriptors. Several artificial neural networks (ANN) models were developed to predict the stiffness and juiciness of apple fruits. At the recording time of 1 s and 680 nm laser diode, the ANN models developed by texture features showed better performance in prediction of stiffness (r = 0.70 and SEP = 8.4 kN) and juiciness (r = 0.68 and SEP = 2.3 cm2) as compared to the common biospeckle features-based ones (r = 0.25 and SEP = 11.3 kN m−1 for stiffness and r = 0.29 and SEP = 3.1 cm2for juiciness). Furthermore, classification of apples into fresh, semi-mealy, and mealy groups was carried out. At the recording time of 25 s and 680 nm laser, the ANN models developed by texture features classified fresh, semi-mealy, and mealy apples with accuracies of 86.3, 65.9, and 95.5%, respectively. Reduction of the recording time to 1 s resulted in the classification accuracies of 88.2, 68.5, and 85.5% for mealy, semi-mealy, and mealy apples compared to the common biospeckle features-based ANN models with the accuracies of 82.8, 50.7, and 46.8%, respectively. These results indicated that the texture descriptors had better performance at shorter recording times in comparison with the biospeckle features which are commonly used in evaluation of biospeckle images, even though more improvements are still required. [ABSTRACT FROM AUTHOR]

In the last few decades, many studies have been performed with the main hope of utilizing imaging methods so as to detect static (bubble size and shape, color, texture) and dynamic (velocity and stability) features of froth that involve crucial information about the process state in order to assess and monitor the performance of flotation process. Although several types of flotation automated control system problems are being successfully solved using the various techniques of features extraction from froth images, there are still a number of unresolved subjects and obstacles. Hence, a suitable review of these methods is needed. After reviewing the technical aspects of froth images structural features in part 1 of this two-part review paper, this work provides an overview of several different approaches for the sake of analyzing and classifying froth image texture and dynamic features. Finally, we conclude our review by linking concentrate grade and recovery with the froth image features described in this two-part paper. In the close future, it is expected with ever-growing application needs and research progress, image analysis systems are becoming even more effective solutions for monitoring and controlling flotation performance. This review provides a platform for future initiatives and potential developments in this area. [ABSTRACT FROM AUTHOR]

In this study, the effects of drying medium temperature and velocity were surveyed on the image texture features of shrimp (Penaeusspp.) batches in a dryer equipped with a perpendicular dual-view computer vision system (CVS). This was carried out by applying an innovative rotation- and scale-invariant image texture processing approach with the capability of eliminating the effects of sample shrinkage on the visual textural features. Moreover, the variations in image texture parameters were investigated with moisture ratio, color, and geometrical characteristics of the shrimp samples. Drying experiments were conducted at hot air drying (HAD) temperatures of 50–90°C and superheated steam drying (SSD) temperatures of 110–120°C with drying medium velocities of 1–2 m/s. Several configurations of a multilayer perceptron artificial neural network (MLP-ANN) were also used to predict the moisture ratio and the geometrical characteristics of the shrimp batch using the image texture parameters. Generally, the image texture features were significantly affected by drying medium temperatures (p < 0.01), and the effects of drying medium velocities on the textural properties were nonsignificant (p > 0.05). Additionally, the higher drying temperatures generated products with uniform and regular texture patterns. The SSD produced samples with somewhat nonuniform and irregular texture patterns compared with HAD at 90°C. Finally, selected MLP-ANN topologies successfully predicted the moisture ratio and the geometrical characteristics of the shrimp batch using the textural properties with correlation coefficients higher than 0.99. [ABSTRACT FROM AUTHOR]

The present study was aimed to investigate the potential of multispectral images coupled with chemometric tools (PLSDA and PLS-R) for: (1.) discriminating different French blue veined cheeses belonging to four brand products(Fourme d’Ambert, Fourme de Montbrison, Bleu d’Auvergne, and Bleu des Causses)and (2.) predicting some of physicochemical (pH, ash, dry matter, total nitrogen, water soluble nitrogen, Ca2+, Na+, Cl−, and P) and rheological properties (softening and dropping points). The results obtained showed that multispectral imaging system applied to anisotropic blue cheeses succeeded to: (1.) discriminate cheeses based on their blue veins features in spite of the visual similarity of their structure and appearance with percentage of correct classification varying between 30 and 100%; and (2.) predict selected parameters (i.e., Ca2+, Cl−, WSN, dropping, and softening points) sinceR2cv≥ 0.62 and RPD ≥ 1.62 were obtained. Moreover, the predictive results suggested that the image texture of cheese was strongly related to its physicochemical composition and rheological characteristics (softening and dropping points). [ABSTRACT FROM PUBLISHER]

Image hashing is a technique that represents an image as a short unidirectional sequence. In this study, we propose a robust color image hashing technique based on the quaternionic local ranking binary pattern (QLRBP). First, the values of the RGB color channels are expressed as a pure quaternion, and the texture image is generated using the phase difference of the Clifford translation quaternion as the source for local binary pattern (LBP) comparison data. Next, mean pooling is used to reduce the dimensionality of the image texture. For security, the pixels of the dimension-reduced image are randomly selected, and each pixel is quantized to obtain the final image hash. Experimental results showed that the proposed scheme resists a variety of ordinary image operations and achieves good balance between robustness and discrimination. In comparison with other state-of-the-art methods, the receiver operating characteristic (ROC) curves of our scheme demonstrate its superiority in terms of content identification. [ABSTRACT FROM AUTHOR]

This study aims to improving long-term post-fire environment assessment. It proposes a method for monitoring fire impact using Sentinel-2 satellite data by combining spectral and textural features of land cover types inside a post-fire study sites. Specific objectives were to 1) test stability of the burnt area index for Sentinel-2 (BAIS2) for identification of burn in study sites, 2) investigate the optimal feature combination for mapping land covers inside study sites, and 3) assess and analyse dynamic in land covers of study sites. BAIS2 was shown independent on date acquisition of satellite images to distinguish forest burn from other land covers over the analysed May–September vegetation period. Texture of study site improved the classification results. The most accurate classification method for identification of study sites land covers (with 0.84 Kappa coefficient and 0.86 overall accuracy) was based on combination of Sentinel-2 bands, BAIS2, and texture by Fourier transform. Analysis of vegetation recovery within the study sites demonstrated different recovery rates. Natural regeneration of pine was not observed, during three to six years of observations following fire events. The proposed method and findings can support planning of forest management measures needed to effectively restore forest cover. [ABSTRACT FROM AUTHOR]

The transition zone between the boreal forest and Arctic tundra, the forest-tundra ecotone (FTE), is an area of high ecological and climatological significance. Despite its importance, a globally consistent high spatial resolution mapping is lacking. Accurate mapping of the FTE requires the use of satellite remote sensing data. Here we use the Landsat Vegetation Continuous Fields (VCF) product and reference point data to derive the location and characteristics of the FTE. An image texture-based supervised classification scheme is developed based on a study area in Central Eurasia to statistically exploit the spatial patterns of the transition zone. Texture statistics for the VCF image are derived from the grey-level co-occurrence matrix (GLCM) based on which the study area is classified into forest, tundra, and FTEs. Adaptive parameterization is implemented to achieve optimal classification performance in the study area. This method is further applied to six additional study areas around the circumarctic region to test its adaptability. In all study areas, this method achieves better FTE delineation results than previously reported methods, showing better classification accuracies (average of 0.826) and more realistic and complete representation of the FTE as shown by visual examination. This shows the universal applicability of the method and it is potential to be used to achieve more detailed and accurate circumarctic mapping of the FTE, which could serve as the basis of time series analysis of FTE positions, eventually contributing to a better understanding of the inter-relations between climate change and shifts in sub-arctic vegetation. [ABSTRACT FROM AUTHOR]

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha−1for PFversus98.5 ± 21.4 ton ha−1for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = −0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA. [ABSTRACT FROM AUTHOR]

Stereo dense image matching normally refers to per-pixel correspondence search between stereo pairs. However, their matching results actually depend on matching window sizes. Large windows usually obtain robust matching results in weak-textured regions, but serious mismatches in depth/disparity (parallax in the epipolar space) inconsistency regions. Small windows compute accurate matching results in depth/disparity inconsistency regions, but it may contain high matching uncertainties in weak-textured regions. To improve matching accuracies, this letter focuses on adaptively selecting appropriate matching windows for each pixel. In general, we propose a window size selection network (WSSN) with the basic assumption that appropriate window sizes are related to image textures and depth/disparity variations. WSSN firstly extracts both image texture features and disparity features by convolutional neural network and then utilizes the fully connected layers to conduct optimal window size selection. Experiments on an aerial image dataset show that our proposed method is capable of selecting appropriate matching windows for each pixel. It achieved the highest matching accuracy when compared with the matching results of a series of fixed matching windows and a state-of-the-art textured based window selection method. [ABSTRACT FROM AUTHOR]

Nowadays, an important problem in combining vector data and imagery is that they rarely align. This problem can become particularly acute in the case of cadastral systems. In this study, and as part of the partnership between the Universities of Jaén and Adam Mickiewicz (Poznań), we provide a methodological proposal to assess the conflation procedures between cadastral vector data and imagery, improving the alignment between both data sets. To do this, we use an automatic alignment algorithm which detects road intersections from both data sets as control points by using image texture characterisation. With this method, we first train the system on the imagery to learn the road texture distribution, then we can obtain its segmentation according to its texture, and finally the system locates road intersection points. The last step is to align vector data and imagery by using different techniques. This algorithm is based on an earlier one, detailed in [Ruiz, J.J., Rubio, T.J., and Ureña, M.A., 2011b. Automatic extraction of road intersections from images in conflation processes based on texture characterization. Survey review, 43 (321), 212-225.]. However, in the updated version we have solved the problem of not-well-defined intersection points, resulting in a substantial increase in the number of intersection points employed for the final adjustment to align both products and in a reduction of the computation time. On the other hand, the positional uncertainty assessment of parcel boundary lines both before and after applying our alignment procedure between them is provided. With regard to the experimental results, in the case of Polish cadastral data this procedure allows for significant improvement in the alignment between imagery and cadastral parcels boundaries. [ABSTRACT FROM AUTHOR]

This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km2 (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km2; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D 2, adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands. [ABSTRACT FROM AUTHOR]

Lidar (light detection and ranging) has demonstrated the ability to provide highly accurate information on forest vertical structure; however, lidar data collection and processing are still expensive. Very high spatial resolution optical remotely sensed data have also shown promising results to delineate various forest biophysical properties. In this study, our main objective is to examine the potential of Quickbird (QB) imagery to accurately estimate forest canopy heights measured from small-footprint lidar data. To achieve this, we have developed multiscale geographic object-based image analysis (GEOBIA) models from QB data for both deciduous and conifer stands. In addition to the spectral information, these models also included (1) image-texture [i.e., an internal-object variability measure and a new dynamic geographic object-based texture (GEOTEX) measure that quantifies forest variability within neighboring objects] and (2) a canopy shadow fraction measure that acts as a proxy of vertical forest structure. A novel object area-weighted error calculation approach was used to evaluate model performance by considering the importance of object size. To determine the best object scale [i.e., mean object size (MOS)] for defining the most accurate canopy height estimates, we introduce a new perspective, which considers height variability both between- and within-objects at all scales. To better evaluate the improvements resulting from our GEOBIA models, we compared their performance with a traditional pixel-based approach. Our results show that (1) the addition of image-texture and shadow fraction variables increases the model performance versus using spectral information only, especially for deciduous trees, where the average increase of R2 is approximately 23% with a further 1.47 m decrease of Root Mean Squared Error (RMSE) at all scales using the GEOBIA approach; (2) the best object scale for our study site corresponds to an MOS of 4.00 ha; (3) at most scales, GEOBIA models achieve more accurate results than pixel-based models; however, we note that inappropriately selected object scales may result in poorer height accuracies than those derived from the applied pixel-based approach. [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]

Image texture can be an important source of data in the image classi-fication process. Although not as easily measurable as image spectral attributes, image texture has proved in a number of cases to be a valuable source of data capable of increasing the accuracy of the classification process. In remote sensing there are cases in which classes are spectrally very similar, but present distinct spatial distribution, i.e. different textural characteristics. Image texture becomes then an important source of information in the classification process. The aim of this study is (1) to develop and test a supervised image classification method based on the image spatial texture as extracted by the Gabor filtering concept and (2) to investigate experimentally the performance of the classification process as a function of the Gabor filter's parameters. A set of Gabor filters is initially generated for the given image data. The filter parameters related to the relevant spatial frequencies present in the image are estimated from the available samples via the Fourier transform. Each filter generates one filtered image which characterizes the particular spatial frequency implemented by the filter parameters. As a result, a number of filtered images, sometimes referred to as 'textural bands', are generated and the originally univariate problem is transformed into a multivariate one, every pixel being defined by a vector with dimension identical to the number of filters used. The multidimensional image data can then be classified by implementing an appropriate supervised classification method. In this study the Euclidean Minimum Distance and the Gaussian Maximum Likelihood classifiers are used. The adequacy of the selected Gabor filter parameters (namely, the spatial frequency and the filter's spatial extent) are then examined as a function of the resulting classification accuracy. The proposed supervised methodology is tested using both synthetic and real image data. Results... [ABSTRACT FROM AUTHOR]

This study examines the relationship between changes in vegetation cover and surface erosion in a semiarid environment and changes in albedo and texture estimates made from Landsat Multispectral Scanner digital images. Albedo emerges as the single most important indicator of change in vegetation cover, though the addition of texture measures appear to provide further information on the condition of surfaces, whereas albedo change indicates relative increase or decrease in vegetation amount. Analysis of high-frequency components of albedo variation indicates that directional and size biases in vegetation and erosion patterns are important in identifying changes in the distribution of vegetation amount and that these biases further affect image texture related to the development of erosion patterns. As expected, physical explanations of the relationship changes in erosion patterns and temporal changes in image texture are not readily forthcoming. [ABSTRACT FROM AUTHOR]

The potential of multi-spectral visible-near infrared imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred forty muscles of three types (longissimus thoracis, biceps femoris, andsemimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d’Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred forty cube images were collected with nineteen Ligth Emitting Diodes (405 to 1050 nm) using the Videometer Lab2 device. The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the partial least square discriminant analysis performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered. This study demonstrated the promising potential of the visible-near infrared multi-spectral imager to characterize beef meat muscles based on muscle type and its animal origin. [ABSTRACT FROM PUBLISHER]

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images. [ABSTRACT FROM AUTHOR]

The growing interest in the development of small satellites and the demand for high-resolution imaging has made the pointing and drift rate requirements of a satellite more stringent. To achieve high pointing accuracy, star sensors can be used, but their size and weight are too large for small satellites. The need for keeping the overall cost of the spacecraft down and still achieve adequate pointing accuracies has provoked the development of relatively inexpensive and high-performance attitude systems that can provide competitive pointing accuracies during imaging operations. In order to realize such a system, this research describes a novel approach for finding the attitude of a satellite at any arbitrary rotation by using inter-band offsets from a single multi-spectral imager (MSI). For Earth observational imagery, UK Disaster Monitoring Constellation Earth-pointing MSI is used. This research focuses on the potential use of a narrow angle between the bands of a pushbroom sensor for determining the attitude of a spacecraft. The technique investigated does not require ground control points or knowledge of any ground features, but rather estimates the orientation of the platform through analysis of perspective and timing-based distortions between images. These distortions are assumed translational and affine in nature, with two-dimensional shifts being extracted from imagery using a Singular Value Decomposition-based registration scheme. In order to better understand the effect of attitude on imagery, a model was developed for predicting inter-band shifts given an attitude. This was then used to estimate the shifts between imagery at nominal attitude and given a series of simulated manoeuvres. Several simulations have shown that the row and column offsets represented a straight line. Hence, we expressed the row and column shifts in terms of straight line parameters. These geometric attributes are then represented in terms of Euler axis/angle to find the mapping for the elements of the general rotation matrix. Once the mapping is computed for the elements of the rotation matrix, we used the standard equations to determine the angle of rotation and Euler axis. The accuracy of attitude estimates depends on the magnitude of angular separation between the cameras, orientation of spacecraft, sensor resolution, image texture, and image registration method. The technique proposed in this study may however be applied to any satellite with pushbroom sensors that have a discernable along track separation and sufficient overlap. [ABSTRACT FROM AUTHOR]

Remote sensing has been widely adopted to map post-fire burn severity over large forested areas. Statistical regression based on linear or simple non-linear assumptions is typically used to link post-fire forest reflectance with the degree of burn severity. However, this linkage becomes complicated if forests experienced severe mortality caused by pre-fire disease or insect outbreaks, which is likely to occur more frequently as a result of rapid climate change. In an effort to improve the understanding of the relationship between forest reflectance and fire-disease disturbances, this study explored the efficacy of three machine learning techniques, that is, Gaussian process regression (GPR), random forests (RF) and support vector regression (SVR), within a geographic object-based image analysis (GEOBIA) framework to assess burn severity in a forest subject to pre-fire disease epidemics. MASTER [MODIS (Moderate Resolution Imaging Spectroradiometer)/ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)] airborne sensor was applied to collect relatively high spatial (4 m) and high spectral (50 bands) resolution reflectance data. Results show that RF, SVR and GPR models outperformed conventional multiple regression by 48%, 29% and 27%, respectively. Compared to SVR and GPR, RF not only achieved better performance in burn severity assessment, but also demonstrated lower sensitivity to the application of different combinations of remote sensing variables. In addition to Normalized Burn Ratio (NBR), this study further revealed the importance of image-texture (representing spectral variability within and between neighbourhood forest patches) in burn severity mapping over diseased forests. [ABSTRACT FROM PUBLISHER]

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms. [ABSTRACT FROM PUBLISHER]

It is crucial to evaluate human settlements to maintain human-based social development. To accurately extract human settlements land-cover information, this article focuses on the unsupervised image segmentation in high-resolution, remotely sensed imagery. J-based segmentation (JSEG) algorithm can offer good segmentation results, but it is highly dependent on image merging thresholds. In this article, a combined, unsupervised image segmentation algorithm is proposed, in which an initial segmentation result is first produced by JSEG and then the image texture is extracted for later region merging. The experiments on the high-resolution imagery show that, when compared with the results obtained by the original JSEG algorithm and eCognition, the proposed algorithm improves the segmentation accuracy. [ABSTRACT FROM AUTHOR]