Abstract: For PolSAR data, the pixels in the same class may have different appearances because of the topographical slopes and the radar look angle. To improve the image classification performance, a supervised polarimetric synthetic aperture radar image classification method is proposed based on Naive Bayes Combination. In the proposed method, the Naive Bayes Combination is adopted to learn different training samples to get classification surfaces in order to improve the classification results. Firstly, we extract some features and choose some pixels as the original training samples for the classification, and randomly divide the training samples into several training sample subsets. After that, the frame of Naive Bayes combination is obtained based on the training sample subsets. Finally, Naive Bayes Combination gives the final classification results. The support vector machine is used as the basic classifier algorithm in this paper for constructing the Naive Bayes Combination. The experimental results of L-band and C-band data of San Francisco demonstrate the effectiveness and robustness of the proposed method.

Key words: polarimetric synthetic aperture radar (PolSAR), image classification, naive Bayes combination