Abstract: In view of the proposed polarization synthetic aperture radar data classification method is difficult to obtain both the boundary and adjacent information of the ground, and in order to reduce the consumption time of image processing, a super-pixel generation algorithm-inear iterative clustering method is introduced, and the geoclassification of the advanced earth observation satellite SAR multipolar data in Japan is studied. Based on the border area of Pengzhou and Shifang city in Sichuan Province, the paper uses Pauli decomposition to generate RGB false color images and filter them, and then uses linear iterative clustering method to generate superpixels on this basis, and finally uses the support vector machine classification method to select polarization entropy reasonably, The polarization features such as anisotropy and average scattering angle are combined as classification parameters to compare and analyze the classification results of pixel-based and hyper-pixel-based polarization SAR images. Experiments show that the use of superpixels is better than other pixel-based classification methods, the overall accuracy of superpixel classification is 95.23% and the Kappa coefficient is 92.58%.

Key words: super pixels, ALOS PALSAR, PolSAR, lands classification, SVM