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Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition
International Journal of Optics ( IF 1.7 ) Pub Date : 2020-08-01 , DOI: 10.1155/2020/5464010
Chenyu Li 1 , Guohua Liu 2
Affiliation  

This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition. The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes. Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors. This paper reconstructs the test sample based on local dictionaries formed by individual classes. Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure. Therefore, to solve the sparse coefficients, the BSBL is employed. The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance. Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.

中文翻译:

基于局部字典的稀疏贝叶斯学习以实现鲁棒的SAR目标识别

本文将块稀疏贝叶斯学习(BSBL)应用于合成孔径雷达(SAR)目标识别。传统的基于稀疏表示的分类(SRC)在由不同类协作的全局词典上运行。然后,通过重建误差评估测试样本与各种类别之间的相似性。本文基于由各个类组成的局部词典来重构测试样本。考虑到SAR图像的方位角敏感性,局部字典上的线性系数是稀疏的,具有块结构。因此,为了解决稀疏系数,采用了BSBL。所提出的方法可以更好地利用每个类的表示能力,从而有利于识别性能。
更新日期:2020-08-01
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