Robust Distributed-target-based Calibration Method for Polarimetric SAR Using Spherically Truncated Covariance Matrix
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摘要: 常规的基于分布式目标的定标算法通过假设协方差矩阵满足特定形式,并用样本协方差矩阵来估计失真参数。然而,样本协方差矩阵并非稳定的协方差矩阵估计子。尤其是当场景中包含不满足定标算法要求的目标时,样本协方差矩阵会偏离理想形式,以致失真参数估计精确度下降。球形截断协方差矩阵方法能够有效地抑制离群样本对协方差矩阵估计的影响,该文将其引入到极化SAR的定标中,并对其可行性进行了理论分析。最后,利用机载全极化SAR数据,验证了该方法能够有效地降低失真参数估计的不确定度,从而提高失真参数估计的稳健性。Abstract: Conventional distributed-target-based polarimetric calibration algorithms estimate polarimetric distortions by assuming that the measured spatially averaged covariance matrix takes a specific form.However, when the underlying surface contains targets that do not satisfy the assumptions employed by those algorithms, the averaged covariance matrix may deviate from the desired form.As a result, poor estimates of distortion parameters may yield.It is known that spherically truncated covariance matrix is robust to outliers.Thus, we introduce it to the polarimetric SAR calibration routine.Experiment results on the airborne SAR data confirm that this method can effectively reduce the uncertainty of distortion estimates, hence improve the robustness of the calibration.
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Key words:
- SAR /
- Polarimetric /
- Calibration /
- Spherically truncated covariance matrix
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