An Improved AP-Wishart Classifier for Polarimetric SAR Images by Incorporating a Textural Features

Abstract

An improved classifier is presented by imposing a textural feature to solve the problems of vague initial clustering results, low classification accuracy and unchangeable class number in the iterative classifier, based on H/Alpha decomposition and the complex Wishart distribution for polarimetric SAR (Synthetic Aperture Radar) images. First, wavelet decomposition is used to extract texture from polarimetric SAR images. Second, an AP (Affinity Propagation) algorithm is applied to create the initial clustering result. This result is then applied to the iterative classifier based on the complex Wishart distribution to obtain the final result. Two PALSAR (Phased Array type L-band Synthetic Aperture Radar) images from ALOS (Advanced Land Observing Satellite) are used for the experiments carried out on experimental plots in Binhai Prefecture, Yancheng City, Jiangsu Province. The results show that the improved classifier has some merits, including clear initial clustering results, flexible class number and high classification accuracy. The improved classifier has better overall performance than the original, and can be effectively applied to the classification of polarimetric SAR images.