Superpixel-Based Classification With an Adaptive Number of Classes for Polarimetric SAR Images

Abstract

Polarimetric synthetic aperture radar (PolSAR) image classification, an important technique in the remote sensing area, has been deeply studied for a couple of decades. In order to develop a robust automatic or semiautomatic classification system for PolSAR images, two important problems should be addressed: 1) incorporation of spatial relations between pixels; 2) estimation of the number of classes in the image. Therefore, in this paper, we present a novel superpixel-based classification framework with an adaptive number of classes for PolSAR images. The approach is mainly composed of three operations. First, the PolSAR image is partitioned into superpixels, which are local, coherent regions and preserve most of the characteristics necessary for image information extraction. Then, the number of classes and each class center within the data are estimated using the pairwise dissimilarity information between superpixels, followed by the final classification operation. The proposed framework takes the spatial relations between pixels into consideration and makes good use of the inherent statistical characteristics and contour information of PolSAR data. The framework is capable of improving the classification accuracy, making the results more understandable and easier for further analyses, and providing robust performance under various numbers of classes. The performance of the proposed classification framework on one synthetic and three real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for unsupervised classification of PolSAR images.