An Automatic Method for Delimiting Deformation Area in InSAR Based on HNSW-DBSCAN Clustering Algorithm
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Published:2023-08-31
Issue:17
Volume:15
Page:4287
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Han Jianfeng1, Guo Xuefei1, Jiao Runcheng1, Nan Yun1, Yang Honglei2ORCID, Ni Xuan1, Zhao Danning1, Wang Shengyu1, Ma Xiaoxue1, Yan Chi1, Ma Chi1, Zhao Jia1
Affiliation:
1. Beijing Institute of Geological Hazard Prevention, Beijing 100120, China 2. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
Abstract
InSAR (Interferometric Synthetic Aperture Radar) is widely recognized as a crucial remote sensing tool for monitoring various geological disasters because it provides all-day and all-weather monitoring. Nevertheless, the current interpretation methods for InSAR heavily depend on the interpreter’s experience, which hinders efficiency and fails to meet the requirements for the timely detection of geologic hazards. Furthermore, the results obtained through current InSAR processing carry inherent noise interference, further complicating the interpretation process. To address those issues, this paper proposes an approach that enables automatic and rapid identification of deformation zones. The proposed method leverages IPTA (Interferometric Point Target Analysis) technology for SAR data processing. It combines the power of HNSW (Hierarchical Navigable Small Word) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms to cluster deformation results. Compared with traditional methods, the computational efficiency of the proposed method is improved by 11.26 times, and spatial noise is suppressed. Additionally, the clustering results are fused with slope units determined using DEM (Digital Elevation Model), which facilitates the automatic identification of slopes experiencing deformation. The experimental verification in the western mountainous area of Beijing has identified 716 hidden danger areas, and this method is superior to the traditional technology in speed and automation.
Funder
the Project of Beijing sudden geological disaster monitoring and early warning system the early identification and early warning of typical geological disasters in Xishan, Beijing Demonstration Study the intelligent early identification method and prevention countermeasures of typical geological hazards in Beijing
Subject
General Earth and Planetary Sciences
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