Unsupervised Classification of Fully Polarimetric SAR Images Based on Scattering Power Entropy and Copolarized Ratio

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Abstract

This letter presents a new unsupervised classification method for polarimetric synthetic aperture radar (POLSAR) images. Its novelties are reflected in three aspects: First, the scattering power entropy and the copolarized ratio are combined to produce initial segmentation. Second, an improved reduction technique is applied to the initial segmentation to obtain the desired number of categories. Finally, to improve the representation of each category, the data sets are classified by an iterative algorithm based on a complex Wishart density function. By using complementary information from the scattering power entropy and the copolarized ratio, the proposed method can increase the separability of terrains, which can be of benefit to POLSAR image processing. Three real POLSAR images, including the RADARSAT-2 C-band fully POLSAR image of western Xi'an, China, are used in the experiments. Compared with the other three state-of-the-art methods, $\hbox{H}/\alpha$ -Wishart method, Lee category-preserving classification method, and Freeman decomposition combined with the scattering entropy method, the final classification map based on the proposed method shows improvements in the accuracy and efficiency of the classification. Moreover, high adaptability and better connectivity are observed.

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