Target Classification From SAR Imagery Based on the Pixel Grayscale Decline by Graph Convolutional Neural Network
Hongliang Zhu, Nan Lin, Howard Leung, Rocky Leung, Segios Theodoidis
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.