Multiview Feature Extraction and Discrimination Network for SAR ATR

Automatic target recognition (ATR) is the key of synthetic aperture radar (SAR) image interpretation. Due to the superior feature extraction and target classification capabilities, deep learning has been widely used in SAR ATR fields. Most of state-of-the-art SAR ATR methods are proposed for single-view input, however, multi-view SAR images include more abundant classification features. In order to improve the SAR ATR performance, it is necessary to carry out an effective method to extract and discriminate useful features from multi-view SAR images. In this paper, we propose a new SAR ATR method based on multi-view feature extraction and discrimination network, which includes two main components: multi-view feature extraction and multi-view feature discrimination. Multi-view features can be effectively extracted from input SAR images with the feature extraction component. After that, the extracted features are fed into the multi-view feature discrimination component, which aims to gather the features of the same class and separate the features of different classes. Therefore, the proposed method can achieve good multi-view SAR target recognition performance. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate the effectiveness of our method.