An Adaptive Multiview SAR Automatic Target Recognition Network Based on Image Attention

Abstract

The deep neural network has achieved remarkable recognition performance in synthetic aperture radar (SAR) automatic target recognition (ATR) by extracting the discriminative features from massive SAR images. Due to the sensitivity of SAR image to the observation aspect, the multiview ATR method could enhance the robustness of feature representation and improve the recognition performance. However, existing multiview ATR methods suffer from increasing complex structure and heavy computation when the number of input images grows. An adaptive multiview fusion network based on image attention (IA-AMF-Net) compatible with variable number of input images is proposed for SAR ATR in this article. In IA-AMF-Net, first, the depthwise separable convolution is employed to extract the classification features from multiple SAR input images in parallel with the lightweight attribute. Second, the channel feature weight vector of each image is generated and concatenated by applying the squeeze-and-excitation operation to the extracted features. The image attention weights for feature fusion are calculated through softmax normalizing the concatenated channel feature weights of input images. At last, the extracted features from multiview SAR images are fused by the obtained image attention weights. The dimension of fused feature keeps constant regardless of the number of input images, and the attention to the classification features of interested images is enhanced. Experimental results on the moving and stationary target acquisition and recognition dataset show that IA-AMF-Net achieves superior recognition performance under various operating conditions with fewer parameters and lower computational load compared to the other networks.