基于形变先验信息的InSAR数据处理自适应误差校正方法

Yilin Wang;Guangcai Feng;Haipeng Guo;Yunlong Wang;Zhiqiang Xiong;Hongbo Jiang
{"title":"基于形变先验信息的InSAR数据处理自适应误差校正方法","authors":"Yilin Wang;Guangcai Feng;Haipeng Guo;Yunlong Wang;Zhiqiang Xiong;Hongbo Jiang","doi":"10.1109/LGRS.2025.3582950","DOIUrl":null,"url":null,"abstract":"Interferometric synthetic aperture radar (InSAR) is a crucial technology for monitoring large-scale surface deformation. Recent advancements have increasingly emphasized the automation of InSAR data processing. However, terrain complexity, environmental variability, and diverse deformation patterns in wide-area monitoring introduce multiple error sources. Conventional correction models based on singular assumptions struggle to achieve adaptive processing, often resulting in low processing efficiency and distortion of some deformation results. To address these challenges, this study proposes an adaptive error correction method guided by deformation prior information, enhancing automated workflows for wide-area InSAR processing. By integrating prior deformation and terrain information to create mask files, this method adaptively distinguishes deformation signals from error components, enabling precise error correction. A case study conducted in the North China Plain (NCP) demonstrates the method’s adaptive error-correction capabilities. Experimental results indicate that the proposed method achieves robust error separation while maintaining solution accuracy across deformation scales from regional subsidence to localized deformations. This method provides novel algorithmic support for automated InSAR data processing in wide-area applications, significantly improving processing efficiency and result reliability.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Adaptive Error Correction Method for InSAR Data Processing Guided by Deformation Prior Information\",\"authors\":\"Yilin Wang;Guangcai Feng;Haipeng Guo;Yunlong Wang;Zhiqiang Xiong;Hongbo Jiang\",\"doi\":\"10.1109/LGRS.2025.3582950\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Interferometric synthetic aperture radar (InSAR) is a crucial technology for monitoring large-scale surface deformation. Recent advancements have increasingly emphasized the automation of InSAR data processing. However, terrain complexity, environmental variability, and diverse deformation patterns in wide-area monitoring introduce multiple error sources. Conventional correction models based on singular assumptions struggle to achieve adaptive processing, often resulting in low processing efficiency and distortion of some deformation results. To address these challenges, this study proposes an adaptive error correction method guided by deformation prior information, enhancing automated workflows for wide-area InSAR processing. By integrating prior deformation and terrain information to create mask files, this method adaptively distinguishes deformation signals from error components, enabling precise error correction. A case study conducted in the North China Plain (NCP) demonstrates the method’s adaptive error-correction capabilities. Experimental results indicate that the proposed method achieves robust error separation while maintaining solution accuracy across deformation scales from regional subsidence to localized deformations. This method provides novel algorithmic support for automated InSAR data processing in wide-area applications, significantly improving processing efficiency and result reliability.\",\"PeriodicalId\":91017,\"journal\":{\"name\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"volume\":\"22 \",\"pages\":\"1-5\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11050415/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11050415/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

干涉合成孔径雷达(InSAR)是监测大尺度地表变形的关键技术。最近的进展越来越强调InSAR数据处理的自动化。然而,在广域监测中,地形的复杂性、环境的多变性和变形模式的多样性引入了多种误差源。传统的基于奇异假设的校正模型难以实现自适应处理,往往导致处理效率低,一些变形结果失真。为了解决这些挑战,本研究提出了一种基于形变先验信息的自适应误差校正方法,增强了广域InSAR处理的自动化工作流程。该方法通过整合先验变形和地形信息创建掩模文件,自适应区分变形信号和误差分量,实现精确的误差校正。以华北平原为例,验证了该方法的自适应纠错能力。实验结果表明,该方法在保持从区域沉降到局部变形跨变形尺度解的精度的同时,实现了鲁棒误差分离。该方法为广域应用的InSAR数据自动处理提供了新的算法支持,显著提高了处理效率和结果的可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Adaptive Error Correction Method for InSAR Data Processing Guided by Deformation Prior Information
Interferometric synthetic aperture radar (InSAR) is a crucial technology for monitoring large-scale surface deformation. Recent advancements have increasingly emphasized the automation of InSAR data processing. However, terrain complexity, environmental variability, and diverse deformation patterns in wide-area monitoring introduce multiple error sources. Conventional correction models based on singular assumptions struggle to achieve adaptive processing, often resulting in low processing efficiency and distortion of some deformation results. To address these challenges, this study proposes an adaptive error correction method guided by deformation prior information, enhancing automated workflows for wide-area InSAR processing. By integrating prior deformation and terrain information to create mask files, this method adaptively distinguishes deformation signals from error components, enabling precise error correction. A case study conducted in the North China Plain (NCP) demonstrates the method’s adaptive error-correction capabilities. Experimental results indicate that the proposed method achieves robust error separation while maintaining solution accuracy across deformation scales from regional subsidence to localized deformations. This method provides novel algorithmic support for automated InSAR data processing in wide-area applications, significantly improving processing efficiency and result reliability.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信