Polarimetric SAR Feature Extraction With Neighborhood Preservation-Based Deep Learning

As an advanced nonlinear technique, deep learning, which is based on deep neural networks (DNNs), has attracted considerable attentions. In this paper, we propose a novel neighborhood preserved deep neural network (NPDNN) for polarimetric synthetic aperture radar feature extraction and classification. The spatial relation between pixels is exploited by a jointly weighting strategy. Not only the spatial neighbors but also the pixels in the same superpixel are utilized to weight each pixel. This strategy maintains the spatial dependence leading to superior homogeneity of the terrains without extra computational memory. Moreover, a few labeled samples and their nearest neighbors are employed to train the multilayer NPDNN, which preserves the local structure and reduces the number of labeled samples for classification. Experimental results on synthesized and real PolSAR data show that the proposed NPDNN can improve the classification accuracy compared with state-of-the-art DNNs despite a few input samples.