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首页> 外文期刊>Journal of Electromagnetic Waves and Applications >Joint sparse representation for multi-resolution representations of SAR images with application to target recognition
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Joint sparse representation for multi-resolution representations of SAR images with application to target recognition

机译：具有应用于目标识别的SAR图像的多分辨率表示的联合稀疏表示

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摘要

In this paper, we propose a target recognition method for synthetic aperture radar (SAR) images based on multi-resolution representations. Considering that the discriminability of SAR targets may lie on different resolutions, the original images at a fixed resolution are used to generate the multi-resolution representations of the target. Then a multi-resolution dictionary is built, which includes the multi-resolution representations of the training samples. For the test sample, its multi-resolution representations are jointly classified using the joint sparse representation (JSR) model based on the multi-resolution dictionary. The multi-resolution dictionary can not only augment the representation capability of the dictionary but also enhance the robustness of the representation. Furthermore, the JSR can exploit the inner correlations among the multi-resolution representations of the test sample. Therefore, a more precise representation of the test sample can be obtained, which will effectively improve the recognition performance. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) data-set to validate the effectiveness and robustness of the proposed method.
机译：在本文中，我们提出了一种基于多分辨率表示的合成孔径雷达（SAR）图像的目标识别方法。考虑到SAR目标的可辨别性可以呈现不同的分辨率，固定分辨率的原始图像用于生成目标的多分辨率表示。然后构建多分辨率字典，其包括训练样本的多分辨率表示。对于测试样本，其多分辨率表示是使用基于多分辨率字典的关节稀疏表示（JSR）模型共同分类。多分辨率字典不仅可以增强字典的表示能力，而且还增强了表示的鲁棒性。此外，JSR可以利用测试样本的多分辨率表示之间的内部相关性。因此，可以获得测试样品的更精确表示，这将有效地提高识别性能。在移动和静止目标采集和识别（MSTAR）数据集上进行实验，以验证所提出的方法的有效性和鲁棒性。

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