Adaptively Selecting Interferograms for SBAS-InSAR Based on Graph Theory and Turbulence Atmosphere
KAUST DepartmentPhysical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Online Publication Date2020-06-17
Print Publication Date2020
Permanent link to this recordhttp://hdl.handle.net/10754/664327
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AbstractThe spatialoral baseline threshold method is commonly used to select interferograms for the Small BAseline Subset interferometric synthetic aperture radar (SBAS-InSAR) technique. However, this selection strategy is rather empirical and prone to including highly contaminated interferograms or excluding those with high quality. To overcome these limitations, this study first derives the relationship between the measurement accuracy of unknown parameters and the number of selected interferograms with their corresponding qualities. Subsequently, an adaptive interferogram selection method is proposed on the basis of Graph Theory (GT) and the turbulence atmospheric effects of interferogram. This proposed method first identifies and deletes the SAR image that is severely polluted by atmospheric phase. Second, high-quality interferograms are selected for SBAS-InSAR based on their corresponding turbulence atmospheric variance. Compared with the traditional selection method, this approach can significantly reduce the effect of turbulence atmosphere on SBAS-InSAR. A set of simulated experiments and real Sentinel-1A data in Hawaii, United States, validate the good performance of the proposed method.
CitationDuan, M., Xu, B., Li, Z.-W., Wu, W.-H., Wei, J.-C., Cao, Y.-M., & Liu, J.-H. (2020). Adaptively Selecting Interferograms for SBAS-InSAR Based on Graph Theory and Turbulence Atmosphere. IEEE Access, 8, 112898–112909. doi:10.1109/access.2020.3002990
SponsorsSupported in part by the National Science Fund for Distinguished Young Scholars under Grant 41925016, in part by the National Natural Science Foundation of China under Grant 41804008, in part by the National Key Research and Development Program of China under Grant 2018YFC1503603, and in part by the Leading Talents Plan of Central South University under Grant 506030101.
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