Deep Supervised and Contractive Neural Network for SAR Image Classification
Abstract: The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCN (DSCNN) N) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch patch-based based feature extraction model that consists of gray level-gradient co-occurrence occurrence matrix, Gabor, and histogram of oriented gradient grad descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features eatures for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequen Consequently, tly, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based graph spatial regularization is adopted after classificatio classification n to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches.