Automatic Target Recognition for Low-Resolution SAR Images Based on Super-Resolution Network

Abstract

Synthetic aperture radar (SAR) automatic target recognition (ATR) is one of the hottest issue in current research because of its wide application value. However, the low-resolution SAR images will decline the recognition accuracy of targets due to its obscure characteristic, and meanwhile it is difficult to acquire a great number of high-resolution SAR images for extracting clear characteristic. To solve these problems, this paper proposes a method of ATR for low-resolution SAR images based on super-resolution network. Super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN) are utilized for extracting characteristic and classification, respectively. The segmented low-resolution SAR images are enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in SAR image; Then the enhanced SAR images are classified automatically by DCNN. Finally, the effectiveness and the efficiency are verified on the open data set, moving and stationary target acquisition and recognition (MSTAR).