Target Classification From SAR Imagery Based on the Pixel Grayscale Decline by Graph Convolutional Neural Network

Abstract

The target classification from synthetic aperture radar (SAR) imagery is entering a bottleneck stage for the extensive use of a deep learning technology. Researchers have deployed various deep neural networks to extract the target features from the original SAR image in Euclidean space, which requires a large number of training data and cost lots of time to train the deep neural networks well generalized. Aiming at this problem, this letter introduces a novel method of target classification from SAR imagery based on the target pixel grayscale decline by a graph representation, which is different from the conventional deep learning methods so far. We separate the whole grayscale interval of one SAR image into several subintervals and assign a node to represent each pixel with the declined order of pixel grayscale in the subinterval. Then, a graph structure could be constructed to transform the raw SAR image from Euclidean data to graph-structured data. Finally, we construct a graph convolutional neural network to extract the features of graph-structured data we constructed previously and output the target classification result. The experiment result on the MSTAR dataset shows that our method achieved the average classification accuracy with 100%, which surpasses all the state-of-the-art methods for the first time in SAR automatic target recognition field.