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A combined preprocessing scheme for texture based ice classification from SAR images

Research on ice conditions in the lakes and rivers plays an important role in the study of Climate change and Global warming. Satellite images can improve the possibilities for classification of ice as they cover large areas. Numerous researches have shown classification based on texture features can improve the precision of the interpretation. This paper presents a preliminary study of image processing on the ice patterns in synthetic aperture radar (SAR) imagery. Here, analysis is done on the performance of texture features derived from the gray-level co-occurrence matrix based on image enhancement methods. The discrimination ability of the proposed method for texture computation is examined and compared by objective parameters. All experiments are conducted on several SAR images to provide generalizations of the results. This experiment concludes that the best GLCM implementation in representing ice texture is one that utilizes the output derived from fusion of filter and smooth by means of using both kuan and median filters.