Abstract
Multiview synthetic aperture radar (SAR) images contain much richer information for automatic target recognition (ATR) than a single-view one. It is desirable to establish a reasonable multiview ATR scheme and design effective ATR algorithm to thoroughly learn and extract that classification information, so that superior SAR ATR performance can be achieved. Hence, a general processing framework applicable for a multiview SAR ATR pattern is first given in this paper, which can provide an effective approach to ATR system design. Then, a new ATR method using a multiview deep feature learning network is designed based on the proposed multiview ATR framework. The proposed neural network is with a multiple input parallel topology and some distinct deep feature learning modules, with which significant classification features, the intra-view and inter-view features existing in the input multiview SAR images, will be learned simultaneously and thoroughly. Therefore, the proposed multiview deep feature learning network can achieve an excellent SAR ATR performance. Experimental results have shown the superiorities of the proposed multiview SAR ATR method under various operating conditions.
Highlights
A multiview deep feature learning network is presented for synthetic aperture radar (SAR) automatic target recognition (ATR) which is based on the above-mentioned framework and can simultaneously learn both the intra-view and inter-view features of multiview SAR images
ATR performance of the proposed multiview deep feature learning network will be evaluated
We will extensively assess the performance of the proposed multiview deep network under different SAR ATR operating conditions
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. If the classification information could be effectively exploited or learned from the multiview SAR images, the SAR ATR performance may be significantly improved Inspired by this thought, a number of novel methods using multiview inputs have been proposed in recent years, which are of high recognition accuracy. Some multiview SAR ATR methods are proposed based on various deep neural network architectures [43,44,45], which could achieve outstanding recognition results under different operating conditions. The proposed network can take advantage of comprehensive and significant classification information from multiview SAR images and achieve high target recognition accuracy. (3) Compared with the available SAR ATR methods, the proposed deep neural network can achieve excellent ATR performances under various operating conditions but with limited raw SAR data for training sample generation.
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