Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

<p><strong>Abstract.</strong> The Advanced Land Observing Satellite (ALOS) is developed by the Japanese Aerospace Exploration Agency (JAXA) which was launched in the year 2006 for the Earth observation and exploration purpose. The ALOS was carrying PRISM, AVNIR-2 and PALSAR sensors for this purpose. PALSAR is L-Band synthetic aperture radar (SAR). The PALSAR sensor is designed in a way that it can work in all weather conditions with a resolution of 10 meters. In this research work we have made an investigation on the accuracy obtained from the various supervised classification techniques. We have compared the accuracy obtained by classifying the ALOS PALSAR data of the Roorkee region of Uttarakhand, India. The training ROI’S (Region of Interest) are created manually with the assistance of ArcGIS Earth and for the testing purpose, we have used the Global positioning system (GPS) coordinates of the region. Supervised classification techniques included in this comparison are Parallelepiped classification (PC), Minimum distance classification (MDC), Mahalanobis distance classification (MaDC), Maximum likelihood classification (MLC), Spectral angle mapper (SAM), Spectral information divergence (SID) and Support vector machine (SVM). Later, through the post classification confusion matrix accuracy assessment test is performed and the corresponding value of the kappa coefficient is obtained. In the result, we have concluded MDC as best in term of overall accuracy with 82.3634% and MLC with a kappa value of 0.7591. Finally, a peculiar relationship is developed in between classification accuracy and kappa coefficient.</p>

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