首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Polarimetric Target Decomposition Based on Attributed Scattering Center Model for Synthetic Aperture Radar Targets
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Polarimetric Target Decomposition Based on Attributed Scattering Center Model for Synthetic Aperture Radar Targets

机译:基于属性散射中心模型的合成孔径雷达目标极化目标分解

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In this letter, a novel polarimetric target decomposition (PTD) method based on the attributed scattering center (ASC) model is proposed for man-made targets in synthetic aperture radar (SAR) images. By extracting attributed parameters, polarimetric characteristics of targets can be exploited by performing PTD on the extracting parameters of ASCs instead of pixels in conventional PTD algorithms. As a result, the integrity of target components is enhanced, leading to a reliable analysis on the polarimetric scattering mechanisms of SAR targets. In the proposal, an attributed parameters extraction method based on joint exploitation of multiple polarimetric channels and a target discriminating method based on a constant-false-alarm threshold are developed to improve its robustness in strong noise scenarios. Experimental results confirm the effectiveness of the proposed algorithm.
机译:在这封信中,针对合成孔径雷达(SAR)图像中的人造目标,提出了一种基于属性散射中心(ASC)模型的新型极化目标分解(PTD)方法。通过提取属性参数,可以通过对ASC的提取参数(而不是常规PTD算法中的像素)执行PTD来利用目标的偏振特性。结果,增强了目标部件的完整性,从而导致对SAR目标的偏振散射机理的可靠分析。该提案提出了一种基于联合利用多极化通道的属性参数提取方法和基于恒定虚警阈值的目标判别方法,以提高其在强噪声场景下的鲁棒性。实验结果证实了该算法的有效性。

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