SEMI-SUPERVISED CLASSIFICATION BASED ON ANCHOR-SPATIAL GRAPH FOR LARGE POLARIMETRIC SAR DATA

Recently a few works of semi-supervised learning methods based on graph have been proposed for remote sensing. The common idea of these methods are that they build a graph using the samples of the image. Most of their time complexity is relatively large, and they ignore the spatial information of the image, which leads to unsatisfactory classification results. this paper proposes a novel semi-supervised classification method based on anchor-spatial graph for large PolSAR data. Firstly the unsupervised Wishart clustering is performed to select representative samples, which served as anchors according to the least distance between samples. Then an anchor graph is built using the selected anchors according to the multiple features of the samples. And it is further combined with the spatial information of the samples to construct an anchor-spatial graph. Finally the class information from small quantities of labeled samples propagates to the unlabeled ones. Experimental results show that the proposed method has a low time complexity compared with existing works and it could effectively cut down the processing time for large PolSAR data meanwhile keeps the classification accuracy.