A Hierarchical Multi-Temporal InSAR Method for Increasing the Spatial Density of Deformation Measurements
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AbstractPoint-like targets are useful in providing surface deformation with the time series of synthetic aperture radar (SAR) images using the multi-temporal interferometric synthetic aperture radar (MTInSAR) methodology. However, the spatial density of point-like targets is low, especially in non-urban areas. In this paper, a hierarchical MTInSAR method is proposed to increase the spatial density of deformation measurements by tracking both the point-like targets and the distributed targets with the temporal steadiness of radar backscattering. To efficiently reduce error propagation, the deformation rates on point-like targets with lower amplitude dispersion index values are first estimated using a least squared estimator and a region growing method. Afterwards, the distributed targets are identified using the amplitude dispersion index and a Pearson correlation coefficient through a multi-level processing strategy. Meanwhile, the deformation rates on distributed targets are estimated during the multi-level processing. The proposed MTInSAR method has been tested for subsidence detection over a suburban area located in Tianjin, China using 40 high-resolution TerraSAR-X images acquired between 2009 and 2010, and validated using the ground-based leveling measurements. The experiment results indicate that the spatial density of deformation measurements can be increased by about 250% and that subsidence accuracy can reach to the millimeter level by using the hierarchical MTInSAR method.
All Author(s) ListLi T, Liu GX, Lin H, Jia HG, Zhang R, Yu B, Luo QL
Journal nameRemote Sensing
Volume Number6
Issue Number4
PublisherMDPI AG
Pages3349 - 3368
LanguagesEnglish-United Kingdom
Keywordsdistributed target; hierarchical processing strategy; multi-temporal InSAR; Pearson correlation coefficient
Web of Science Subject CategoriesRemote Sensing; REMOTE SENSING

Last updated on 2021-15-10 at 00:09