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SAR Automatic Target Recognition Using Joint Low-Rank and Sparse Multiview Denoising
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DOI：10.1109/LGRS.2018.2851146.png)
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Abstract
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In recent years, many researchers have focused on the automatic target recognition problem for high-resolution synthetic aperture radar (SAR) systems. Most have directly employed the training data as the dictionary, which introduces error from speckle noise. In this letter, a joint low-rank and sparse multiview denoising (JLSMD) dictionary is generated, which combines multiview training samples for denoising. To extract the dictionary, we fully consider the low-rank property of multiview target images and the sparsity of speckle noise for SAR systems. The designed dictionary is more accurate than the training data in representing the target. With the help of the proposed JLSMD dictionary, we develop three algorithms based on the sparse representation classification and the support vector machine approach. We carry out experiments on the moving and stationary target acquisition and recognition public data set to evaluate the excellent performance of the proposed methods against several state-of-the-art methods, including deep learning methods.
Keywords:
Joint low-rank and sparse multiview denoising (JLSMD)
sparse representation classification (SRC)
support vector machine (SVM)
synthetic aperture radar automatic target recognition (SAR ATR)
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