Amplitude and Texture Feature Based SAR Image Classification with A Two-Stage Approach

This paper presents an SAR image classification approach that takes advantage of both amplitude and texture features. The proposed approach is based on superpixels obtained with some over-segmentation methods, and consists of two stages. In the first stage, the SAR image is classified with amplitude and texture feature used separately. Specifically, we use statistical model based maximum-likelihood method for amplitude based classification. Meanwhile, we classify the SAR image with the support vector machine (SVM) method by taking histograms generated with sparse coded morphological profiles as feature. To combine classification results produced with amplitude and texture features, a second refine stage is proposed based on the conditional random field (CRF) method. We define the CRF based on region adjacent graph (RAG) of superpixels. The unary term of the CRF is based on fusing classification scores produced by two classifiers in the first stage. Therefore, both of amplitude and texture information are used for the final classification. The graph cut (GC) algorithm is used to optimize the CRF model. We show experimental results on real SAR data, which verify the effectiveness of the proposed approach.