Joint sparse representation of complementary components in SAR images for robust target recognition

Shuguang Miao and Xiaowen Liu

Journal of Electromagnetic Waves and Applications, 2019, vol. 33, issue 7, 882-896

Abstract: An automatic target recognition (ATR) method of synthetic aperture radar (SAR) images is proposed by joint sparse representation (JSR) of the complementary components from the original SAR image. The shadow and target image are generated from the original image. A simple but effective segmentation algorithm is designed to separate out the shadow region. By replacing the shadow region with randomly selected background pixels in the original image, the target image is generated. Afterwards, the two components together with the original image are jointly classified based on JSR. Due to the extended operating conditions (EOCs) in SAR ATR, the shadow or target region may be corrupted. In this case, the sole use of the original image may bring some interference caused by the corruption. As a remedy, the joint use of the three components can effectively improve the robustness of the ATR method to various EOCs by complementing each other. To quantitatively evaluate the proposed method, experiments are conducted on the moving and stationary target acquisition and recognition dataset under various conditions. The results demonstrate the effectiveness and robustness of the proposed method.

Date: 2019
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DOI: 10.1080/09205071.2018.1496038

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