Abstract

In order to obtain good classification performance of polarimetric synthetic aperture radar (PolSAR) images, many labeled samples are needed for training. However, it is difficult, expensive, and time-consuming to obtain labeled samples in practice. On the other hand, unlabeled samples are substantially cheaper and more plentiful than labeled ones. In addressing this issue, semisupervised learning techniques are proposed. In this paper, a novel semisupervised algorithm based on an improved cotraining process is proposed for PolSAR image classification. First, we propose an indirect analysis strategy to analyze the nature of sufficiency and independence between two different views for cotraining. Then, an improved cotraining process with a new sample selection strategy is presented, which can effectively take advantage of unlabeled samples to improve the performance of classification, particularly when labeled samples are limited. Finally, a new postprocess method based on a similarity principle and a superpixel algorithm is developed to improve the consistency of the classification. Experimental results on three real PolSAR images show that our proposed method is an effective classification method, and is superior to other traditional methods.

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