| SAR干涉图降噪的稳健协方差矩阵分解法 |
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| 引用本文： | 赵超英,王宝行.SAR干涉图降噪的稳健协方差矩阵分解法[J].测绘学报,2019,48(1):24-33. |
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| 作者姓名： | 赵超英 王宝行 |
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| 作者单位： | 长安大学地质工程与测绘学院,陕西 西安 710054;地理国情监测国家测绘地理信息局工程技术研究中心,陕西 西安 710054;长安大学地质工程与测绘学院,陕西 西安,710054 |
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| 基金项目： | 国家自然科学基金（41731066；41628401；41504005；41372375） |
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| 摘 要： | 干涉图降噪在InSAR技术应用中发挥着重要作用,若降噪效果不好将引起干涉图相位解缠的误差,并进一步导致DEM或形变结果的错误。由于干涉图分辨单元的信号(相位)是由分辨单元内多个散射体的回波信号(相位)叠加而成,本文针对单一主导散射体的散射模型(永久性散射体模型)和只考虑一种散射机制的分布式散射体模型相位的特点,对多基线SAR数据估计的协方差矩阵采用特征值分解的方法来分离相位中的噪声,通过提取最大特征值对应的特征向量(相位),从而实现干涉图降噪的目的。而对于协方差矩阵估计时引入的异质点,本文采用了一种稳健的协方差矩阵估计方法。通过覆盖山西清徐地面沉降形变区的8景真实TerraSAR数据试验验证了该方法的有效性。结果表明该方法比改进的Goldstein滤波方法在相干性提高、有效目标点增加两方面均有显著提高,特别在低相干区域由于相干点的增加也获取了更多的形变监测信息。
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| 关 键 词： | 同质点 稳健估计 协方差矩阵分解 干涉图去噪 |
| 收稿时间： | 2017-07-10 |
| 修稿时间： | 2018-09-21 |
SAR interferogram denoising based on robust covariance matrix decomposition |
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| ZHAO Chaoying,WANG Baohang.SAR interferogram denoising based on robust covariance matrix decomposition[J].Acta Geodaetica et Cartographica Sinica,2019,48(1):24-33. |
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| Authors: | ZHAO Chaoying WANG Baohang |
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| Institution: | 1. School of Geology Engineering and Geomatics, Chang'an University, Xi'an 710054, China;
2. Engineering Research Center of National Geographic Conditions Monitoring, National Administration of Surveying, Mapping and Geoinformation, Xi'an 710054, China |
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| Abstract: | Interferogram denoising plays an important role to the application of InSAR technique. If the phase noise cannot be well filtered, the phase unwrapping error is frequently arisen, which will further result in the mistakes in the DEM product and the deformation result. The complex value of each SAR resolution unit is superimposed by the phases from different scatterers, so the paper focuses on the characteristics of single dominant phase scattering model (the permanent scatterer) and traditional distributed scatterer of single scattering mechanism. Then the robust covariance matrix, estimated based on multi-baseline SAR data, is decomposed and the eigenvector corresponding to the maximum eigenvalue is chosen as the filtered phase. Besides, the covariance matrix is robustly estimated by weighted averaging the heterogeneous points. This method can operate better than the improved Goldstein filter algorithm in the terms of coherence improvement and effective coherent targets increasing, especially in the low-coherence regions. Eight real TerraSAR-X data over one land subsidence region, Qingxu, Shanxi verifies the advantages of our new method. |
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| Keywords: | homogeneous point robust estimation covariance matrix decomposition interferogram denoising |
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