摘要
合成孔径雷达干涉测量(interferometric synthetic aperture Radar,InSAR)技术在矿区植被覆盖密集且存在大梯度地表形变复杂环境下进行监测时,存在监测点数量不足、监测精度不高等问题。针对这些问题,该文提出一种Stacking技术辅助下的改进分布式目标InSAR(distributed scatterer InSAR,DS-InSAR)方法。该方法采用置信区间假设检验算法识别同质像元并基于相位三角剖分算法完成相位优化,随后去除先期利用Stacking技术模拟的线性形变相位获取残余相位,进而稀疏形变相位条纹,提高后续DS-InSAR处理框架中时空滤波与三维解缠结果的精确性,最终补偿模拟相位获得完整形变场。通过处理2015年10月—2016年3月期间覆盖新巨龙煤矿的Sentinel-1A合成孔径雷达(synthetic aperture Radar,SAR)影像,解译了该时段内矿区时序地表形变特征,得到以下结论:监测期间,矿区存在3处显著形变,雷达视线向最大累积形变量达到-313 mm;所提方法相较常规短基线集干涉测量(small baseline subset InSAR,SBAS-InSAR)技术能够反演出分布更加均匀的监测点位信息,其密度约是SBAS-InSAR的12.9倍;对比水准数据的均方根误差(root mean squared error,RMSE)约为6.82 mm,精度较SBAS-InSAR提高了约3.0 mm。
Interferometric Synthetic Aperture Radar(InSAR)faces the challenges of the insufficient number of monitoring points and low monitoring accuracy when applied to complex environments with dense vegetation and large-gradient surface deformation in a mining area.To address these challenges,this study proposed an improved distributed scatterer InSAR(DS-InSAR)method assisted by stacking technology.This method identified statistically homogenous pixels using a confidence interval hypothesis test and achieved phase optimization utilizing a phase triangulation algorithm.Subsequently,the residual phases were derived by removing the linear deformation phases determined via stacking-based simulation.This step contributed to sparse deformation phase fringes,thereby enhancing the accuracy of spatiotemporal filtering and three-dimensional phase unwrapping within the subsequent DS-InSAR processing framework.Finally,the simulated phases were compensated,and thus complete deformation fields were determined.By processing Sentinel-1A SAR images covering the Xinjulong Coal Mine from October 2015 to March 2016,this study interpreted the time-series surface deformation characteristics in the mining area during this period.The findings revealed three significant deformation sites in the mining area,with a maximum cumulative radar line-of-sight(LOS)deformation of up to-313 mm.Compared to conventional small Baseline Subset(SBAS)InSAR,the proposed method yielded more uniformly distributed monitoring points via inversion,with a density approximately 12.9 times higher.The root mean squared error(RMSE)of the inversion was approximately 6.82 mm relative to leveling data,representing an accuracy improvement of about 3.0 mm compared to the SBAS results.
作者
李志
张书毕
李鸣庚
陈强
卞和方
李世金
高延东
张艳锁
张帝
LI Zhi;ZHANG Shubi;LI Minggeng;CHEN Qiang;BIAN Hefang;LI Shijin;GAO Yandong;ZHANG Yansuo;ZHANG Di(School of Environment and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China;China Railway Shanghai Design Institute Group Corporation Limited,Shanghai 200040,China;Yankuang energy Group Company Limited Jining No.3 coal mine,Jining 272000,China)
出处
《自然资源遥感》
北大核心
2025年第4期12-20,共9页
Remote Sensing for Natural Resources
基金
国家自然科学基金“高寒关键生态走廊滑坡机理与天空地协同智能监测预警方法”(编号:U22A20569)
“融合GNSS和InSAR数据的动态节点基水汽层析理论与同化方法研究”(编号:42271460)
“基于机器学习与多源数据融合的多基线相位解缠方法研究”(编号:42001409)
中国博士后科学基金资助项目“基于机器学习与数据压缩的矿区DSInSAR数据处理方法研究”(编号:2022M723376)
江苏省卓越博士后计划“‘学习型’理论与时间概率积分模型驱动下DSInSAR矿区监测研究”(编号:2023ZB277)共同资助。
