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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Urban Area Man-Made Target Detection for PolSAR Data Based on a Nonzero-Mean Statistical Model
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Urban Area Man-Made Target Detection for PolSAR Data Based on a Nonzero-Mean Statistical Model

机译：基于非零均值统计模型的PolSAR数据人工目标检测

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摘要

Numerous opportunities for advancement exist in the field of man-made target detection using synthetic aperture radar (SAR). With the development of SAR sensors, the high spatial resolution violates the validity of the zero-mean assumption, particularly for urban applications. In 2012, we refined the conventional azimuth stationarity extraction method by adopting a nonzero-mean model—the Rician distribution—to better adapt to the high-resolution SAR imagery of urban areas and improved the man-made target detection result significantly. However, the model cannot make full use of the multipolarization information because it is calculated with the amplitude data. This study extends the method by employing another nonzero-mean model—the multivariate normal distribution—to consider the scattering vector of polarimetric SAR (PolSAR) images. The proposed method achieves an excellent performance with overall accuracy of 88.76%. Two previous methods, including the PolSAR method using the zero-mean model and the single polarization method using the nonzero-mean model, are also involved in the comparisons. The effectiveness of applying nonzero-mean models to urban area SAR images is well demonstrated by experimental results.
机译：在使用合成孔径雷达（SAR）进行人造目标检测领域，存在许多进步的机会。随着SAR传感器的发展，高空间分辨率违反了零均值假设的有效性，特别是对于城市应用。 2012年，我们通过采用非零均值模型Rician分布改进了传统的方位平稳性提取方法，以更好地适应市区的高分辨率SAR图像，并显着改善了人造目标检测结果。但是，该模型不能充分利用多极化信息，因为它是根据振幅数据计算出来的。这项研究通过使用另一个非零均值模型（多元正态分布）来扩展该方法，以考虑极化SAR（PolSAR）图像的散射矢量。所提出的方法具有优良的性能，总精度为88.76％。比较中还涉及两种先前的方法，包括使用零均值模型的PolSAR方法和使用非零均值模型的单极化方法。实验结果很好地证明了将非零均值模型应用于市区SAR图像的有效性。

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