POLARIMETRIC SAR IMAGES CLASSIFICATION USING DEEP BELIEF NETWORKS WITH LEARNING FEATURES

A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using these patches. Thus we can get the features for each pixel from each dimension in the 9-dimentional space. Finally, the learned features and the elements of coherent matrix are combined to train a 3-layers DBNs for PolSAR image classification. Experimental results show that the proposed method is efficient and effective for PolSAR image classification.