SAR interferogram denoising based on robust covariance matrix decomposition

doi:10.11947/j.AGCS.2019.20170394

Abstract

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.

Key words: homogeneous point, robust estimation, covariance matrix decomposition, interferogram denoising

CLC Number:

P237

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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