Multi-Temporal InSAR Deformation Monitoring Zongling Landslide Group in Guizhou Province Based on the Adaptive Network Method

Yu Zhu, Bangsen Tian (), Chou Xie, Yihong Guo, Haoran Fang, Ying Yang, Qianqian Wang, Ming Zhang, Chaoyong Shen and Ronghao Wei
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Yu Zhu: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Bangsen Tian: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Chou Xie: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Yihong Guo: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Haoran Fang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Ying Yang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Qianqian Wang: School of Earth Sciences and Resources, China University of Geosciences, Beijing, 100083, China
Ming Zhang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Chaoyong Shen: The Third Surveying and Mapping Institute of Guizhou Province, Guiyang 550004, China
Ronghao Wei: Zhejiang Institute of Hydraulics & Estuary (Zhejiang Institute of Marine Planning and Design), Hangzhou 310017, China

Sustainability, 2023, vol. 15, issue 2, 1-24

Abstract: Due to the influence of atmospheric phase delays and terrain fluctuation in complex mountainous areas, traditional PS-InSAR technology often fails to select enough measurement points (MPs) and loses effective MPs during phase unwrapping. To solve this problem, this paper proposes an adaptive network construction algorithm, which combines the permanent scatterer (PS) points with the distributed scatterer (DS) points. Firstly, to ensure the extraction quality of the DS points, the covariance matrix of DS points is estimated robustly. Secondly, based on the traditional Delaunay triangulation network, an adaptive network construction method is proposed, which can adaptively increase edge redundancy and network connectivity by considering the edge length, edge coherence, edge number, and spatial distribution. Finally, a total of 31 RADARSAT-2 SAR images that cover the Zongling landslide group in Guizhou Province were used to prove the effectiveness of proposed method. The results show that the quantity of available DS points can be increased by 23.6%, through the robust estimation of the covariance matrix. In addition, it is demonstrated that the proposed network construction algorithm can balance the number, distribution, and quality of edges in the dense and sparse areas of MPs adaptively. This adaptive network construction approach can maintain good connectivity and avoid losing effective MPs to the greatest extent, especially when the scattering points are far away from the reference points. In short, the proposed algorithm improves the number of effective MPs and accuracy of phase unwrapping.

Keywords: PS-InSAR; adaptive network; covariance matrix; robust estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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