Joint Polarimetric-Adjacent Features Based on LCSR for PolSAR Image Classification

Abstract

Image classification is a critical and important application in PolSAR image interpretation. Finding a feature extraction method, which can effectively describe the characteristics of the target, is an important basis for image classification. In addition to unique polarimetric features of PolSAR system, spatial adjacent features of image also need to be considered. So in this article, a joint polarimetric-adjacent features extraction method based on local convolution sparse representation is proposed for PolSAR image classification. Firstly, this article uses convolutional sparse representation to achieve the convolution of the image filters and the feature responses so as to achieve the effective combination of the polarimetric and adjacent information of the image. Meanwhile, construct and train the dictionary using local strategy in the original domain to avoid the high computational complexity and the confusion of different grounds caused by global dictionary. Finally, support vector machine (SVM) is used to combine the extracted features to achieve the classification. Three sets of full polarimetric data are used and the experiment results prove that the proposed method can effectively combine the polarimetric and adjacent information of data and have a good performance in PolSAR image classification.