Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR
LIN Jiahui1,2,3(), LIU Guang1,2,3, FAN Jinghui4(), ZHAO Hongli4, BAI Shibiao5,6, PAN Hongyu1,2,3
1. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China 2. International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China 3. University of Chinese Academy of Sciences, Beijing 100049, China 4. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China 5. College of Marine Sciences and Engineering, Nanjing Normal University, Nanjing 210023, China 6. Key Laboratory of Mountain Hazards and Earth Surface Processes, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
Surface subsidence caused by the exploitation of mineral resources must be considered during the development and utilization of land and space in mining areas. Furthermore, it serves as a significant indication of underground areas subjected to illicit mining. The exploitation of mineral resources is generally conducted in widespread, uneven, and dispersed areas, making it necessary to quickly and accurately identify and extract the spatial distribution of mining subsidence in large areas. This study determined the multitemporal differential interferometric phase diagram of mining areas using the differential interferometric synthetic aperture Radar (D-InSAR) technique. Furthermore, it trained networks for the intelligent identification of mining subsidence by employing deep-learning FCN-8s, PSPNet, Deeplabv3, and U-Net models. The results show that the U-Net model enjoys a high detection accuracy and a short detection time. To improve the semantic segmentation and extraction accuracy of information about mining subsidence, this study introduced the efficient channel attention (ECA) module into the conventional U-Net model during the training. Compared with the conventional model, the improved U-Net model increased the intersection over union (IOU) corresponding to mining subsidence by 2.54 percentage points.
基金资助:国家重点研发计划项目“高亚洲和北极积雪-冰川与地质灾害监测技术及示范应用”(2021YFE0116800);中欧龙计划5期合作项目“Integration of multisource remote sensing data to detect and monitoring large and rapid landslides and use of artificial intelligence for cultural heritage preservation”(56796);可持续发展大数据国际研究中心创新研究计划(CBAS2022IRP02);国家自然科学基金项目“青藏高原露天煤矿排土场地形-土壤-植被响应机理及地貌重塑研究”(41977415)
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2023, Vol. 35 
), 刘广1,2,3, 范景辉4(
