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作 者:何原荣 张立衡[1,2] 冯培源 钱志恒 蒿泮霖 陈炳凝 HE Yuanrong;ZHANG Liheng;FENG Peiyuan;QIAN Zhiheng;HAO Panlin;CHEN Bingning(School of Computer and Information Engineering,Xiamen University of Technology,Xiamen,Fujian 361024,China;Big Data Institute of Digital Natural Disaster Monitoring in Fujian,Xiamen University of Technology,Xiamen,Fujian 361024,China)
机构地区:[1]厦门理工学院计算机与信息工程学院,福建厦门361024 [2]厦门理工学院数字福建自然灾害监测大数据研究所,福建厦门361024
出 处:《测绘科学》2024年第12期43-57,共15页Science of Surveying and Mapping
基 金:2023年福建省对外合作项目(2023I0047);龙岩市产学研联合创新项目(2022LYF18001);福建省自然科学基金引导性项目(2024Y0057);福建省住房和城乡建设行业科技研究开发项目(2023-K-27);福建省教育部产学合作协同育人项目(230819260707307,231107551090743);教育部供需对接就业育人项目(2024011223947)。
摘 要:针对InSAR地表形变监测技术在矿区监测中的广泛应用前景,该文首先回顾了InSAR的基本原理、技术进展及卫星发展情况;其次,以矿区监测中应用的主要InSAR方法为切入点,系统梳理了D-InSAR、PS-InSAR、SBAS-InSAR等主流方法的应用情况以及优缺点。然后,从方法改进、多源数据综合分析以及算法优化等多个视角,深入探讨了InSAR技术在矿区形变监测中的最新进展;最后,针对InSAR技术的局限性,结合人工智能(AI)、机器学习、多源遥感及其他观测技术,从数据处理与新技术融合的角度,对未来InSAR技术在矿区地表形变监测中的应用进行了展望。Regarding the broad application prospects of InSAR surface deformation monitoring technology in mining area monitoring,this article first reviews the basic principles,technological progress,and satellite development of InSAR;Secondly,taking the main InSAR methods used in mining area monitoring as the starting point,the application status and advantages and disadvantages of mainstream methods such as D-InSAR,PS InSAR,SBAS InSAR were systematically reviewed.Then,from multiple perspectives such as method improvement,comprehensive analysis of multi-source data,and algorithm optimization,the latest progress of InSAR technology in deformation monitoring in mining areas was deeply explored;Finally,in view of the limitations of InSAR technology,combined with artificial intelligence(AI),machine learning,multi-source remote sensing,and other observation technologies,the future application of InSAR technology in surface deformation monitoring in mining areas is discussed from the perspective of data processing and new technology integration.
分 类 号:P236[天文地球—摄影测量与遥感] P237[天文地球—测绘科学与技术]
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