Robust object-based multi-baseline InSAR

Abstract

Deformation monitoring by multi-baseline repeat-pass synthetic aperture radar (SAR) interferometry is so far the only imaging-based method to assess millimeter-level deformation over large areas from space. Past research mostly focused on the optimal deformation parameters retrieval on a pixel-basis. Only until recently, the first demonstration of object-based urban infrastructures monitoring by fusing SAR interferometry (InSAR) and the semantic classification labels derived from optical images was presented in [1]–[3]. This paper demonstrates a general framework for object-based InSAR parameters retrieval where the estimation of the parameters is achieved in an object-level instead of pixel-wisely. Furthermore, to handle outliers in real data, a robust phase recovery step in prior to the parameters inversion is also introduced. The proposed method outperforms the current pixel-wised estimators, e.g. periodogram, by a factor of as much as several dozens in the accuracy of the linear deformation estimates. [1] Y. Wang and X. X. Zhu, “Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring,” IEEE Trans. Geosci. Remote Sens., 2016. [2] Y. Wang and X. X. Zhu, “InSAR Forensics: Tracing InSAR Scatterers in High Resolution Optical Image,” presented at the Fringe 2015, 2015. [3] Y. Wang and X. X. Zhu, “Semantic Fusion of SAR Interferometry and Optical Image with Application to Urban Infrastructure Monitoring,” presented at the CMRT, France, La Grande Motte, France, 2015.