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Data Augmentation by Multilevel Reconstruction Using Attributed Scattering Center for SAR Target Recognition

机译:归因于散射中心的SAR目标识别的多层重构数据增强

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摘要

The quality of synthetic aperture radar (SAR) images and the completeness of the template database are two important factors in template-based SAR automatic target recognition. This letter gives a solution to the two factors by multilevel reconstruction of SAR targets using attributed scattering centers (ASCs). The ASCs of original SAR images are extracted to reconstruct the target’s image, which not only reduces the noise and background clutters but also keeps the electromagnetic characteristics of the target. Template database are reconstructed at multilevels to simulate various extents of ASC absence in the extended operation conditions. Therefore, the quality of SAR images as well as the completeness of the template database is augmented. Features are extracted from the augmented SAR images, and the classifier is trained by the augmented database for target recognition. Experimental results on the moving and stationary target acquisition and recognition data set demonstrate the validity of the proposed method.
机译:合成孔径雷达(SAR)图像的质量和模板数据库的完整性是基于模板的SAR自动目标识别的两个重要因素。这封信通过使用归因散射中心(ASC)多层重建SAR目标,为这两个因素提供了解决方案。提取原始SAR图像的ASC来重建目标图像,这不仅减少了噪声和背景杂波,而且还保留了目标的电磁特性。在多个级别上重建模板数据库,以模拟扩展操作条件下各种程度的ASC缺失。因此,SAR图像的质量以及模板数据库的完整性得以增强。从增强的SAR图像中提取特征,然后由增强的数据库训练分类器以进行目标识别。在动和静止目标获取和识别数据集上的实验结果证明了该方法的有效性。

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