摘要
基于我国首颗干涉L波段SAR卫星“陆探一号”(LT-1)提出一种融合滑动窗口去噪与最优阈值筛选的自动识别方法,用于消除随机噪声并提取形变区域。在陕西省韩城市的应用表明,该方法成功识别出17处形变区,仅1处误判,最大形变量达-192 mm/28 d(LOS方向)。野外验证结果进一步印证了识别的准确性。该方法显著提升了识别效率与精度,降低了解译工作量,具备广域地质灾害监测的实际应用价值。
With the advancement of InSAR satellite technology and data processing capabilities,it is now possible to acquire rapidly large-scale,high-resolution surface deformation information,providing strong support for geological hazard identification.However,the surge in data volume also increases the complexity of interpretation,highlighting the urgent need for efficient and accurate automatic detection methods.This study proposes an automated identification approach integrating sliding-window denoising and optimal threshold selection,specifically designed for data from China’s first L-band InSAR satellite,“LuTan-1”(LT-1).The method effectively suppresses random noise and extracts deformation regions.Application in Hancheng City,Shaanxi Province,leads to the successful identification of 17 deformation zones,with only one false positive and a maximum deformation of−192 mm over 28 days(in the line-of-sight direction).Field validation further confirmes the reliability of the detected results.The proposed approach significantly improves recognition efficiency and accuracy,reduces interpretation workload,and demonstrates practical value for largescale geological hazard monitoring.
作者
杨帅
唐光民
韩静
朱楠男
周浩
彭林娟
戴可人
YANG Shuai;TANG Guangmin;HAN Jing;ZHU Nannan;ZHOU Hao;PENG Linjuan;DAI Keren(Shaanxi Satellite Application Center for Natural Resources,Xi’an 710054,China;State Key Laboratory of Geological Disaster Prevention and Geological Environmental Protection,Chengdu University of Technology,Chengdu 610059,China;College of Geophysics,Chengdu University of Technology,Chengdu 610059,China;College of Earth and Planetary Sciences,Chengdu University of Technology,Chengdu 610059,China;State Grid Southwest Electric Power Research Institute,Chengdu 610095,China)
出处
《测绘地理信息》
2026年第1期65-70,共6页
Journal of Geomatics
基金
国家自然科学基金(42371462)
四川省自然科学基金杰出青年科学基金(2023NSFSC1909)
国家重点研发计划(2021YFB3901403)。
