Multiview Attention CNN-LSTM Network for SAR Automatic Target Recognition

Synthetic aperture radar (SAR) is a microwave remote sensing system. It has a broad scope of applications in both military and civilian fields. Benefited from the latest advances in deep learning, SAR automatic target recognition technology has made an excellent breakthrough However, most existing methods ignore the large variation of scattering characteristics of SAR target images with different azimuths, which limits the performance and practical application. The SAR images under different azimuths contain distinct feature information, and the images under adjacent azimuths are correlated in terms of features. Therefore, extracting the feature information of images under adjacent azimuths and leveraging their correlation can improve the recognition performance. In this article, we proposed a multiview attention convolutional neural network with long short-term memory (LSTM) network to extract and fuse the features from images with adjacent azimuths. It adopts multiple convolutional modules to extract deep features from each single-view SAR image and spatial attention module to locate the information of the target and suppress the useless noise. Then, the LSTM module performs feature fusion based on the correlation of features obtained from adjacent azimuths. Finally, based on these multiview images, deep features are extracted and fused to obtain precise recognition results. Experiments are performed on the moving and stationary target acquisition and recognition dataset, and the results have verified the effectiveness of the proposed method.