摘要
合成孔径雷达(SAR)影像是生产干涉合成孔径雷达(InSAR)数据的重要资料,其预处理阶段的准确性直接影响到后续地表形变分析的可靠性和精度。目前,在SAR数据预处理过程中存在人工处理工作量大、过程复杂等问题,而现有自动处理方法不够稳定。本研究旨在探索使用Python调用SNAP(Sentinel Application Platform)软件中设计的XML处理流程,实现Sentinel-1A SAR数据的自动化批量预处理。通过对北京某区域数据进行处理验证,证明自动化批量预处理的有效性,预处理后的数据结果质量符合科学研究和应用需求,为地表形变监测和地质灾害预警提供可靠的数据支持,同时为遥感地质灾害领域提供稳定灵活的数据自动化处理思路。
Synthetic Aperture Radar(SAR)imagery serves as essential data for producing Interferometric Synthetic Aperture Radar(In-SAR)data,and the accuracy of its preprocessing stage directly affects the reliability and accuracy of subsequent surface deformation analysis.Currently,SAR data preprocessing involves challenges such as manual labor-intensive tasks and complex procedures,while existing automated methods are not sufficiently stable.This study aims to explore the automation of batch preprocessing of Sentinel-1A SAR data by utilizing Python to invoke the XML processing workflows designed in the Sentinel Application Platform(SNAP)software.Through processing and verification of data from a specific area in Beijing,the effectiveness of automated batch preprocessing has been demonstrated.The quality of the preprocessed data meets the requirements for scientific research and applications,providing reliable data support for surface deformation monitoring and geological hazard early warning.Additionally,this approach offers a stable and flexible automated data processing solution for the field of remote sensing geological hazards.
作者
石成岳
周玉科
王笑影
牛露佳
SHI Chengyue;ZHOU Yuke;WANG Xiaoying;NIU Lujia(School of Geosciences&Surveying Engineering,China University of Mining&Technology(Beijing),Beijing 100083,China;Institute of Geographic Sciences and Nature Resources Research,Chinese Academy of Sciences,Beijing 100101,China;Institute of Atmospheric Environment,CMA,Shenyang 110166,China;School of Earth Sciences and Engineering,Sun Yat-sen University,Zhuhai 519000,China)
出处
《测绘与空间地理信息》
2025年第10期17-19,23,共4页
Geomatics & Spatial Information Technology
基金
国家重点研发计划(2021xjkk0303)
中国气象局沈阳大气环境研究所和辽宁省农业气象灾害重点实验室联合开放基金(2023SYIAEKFZD05)资助。
