Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR

LIN Jiahui; LIU Guang; FAN Jinghui; ZHAO Hongli; BAI Shibiao; PAN Hongyu

doi:10.6046/zrzyyg.2022197

2023 Vol. 35, No. 3

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LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. 2023. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR. Remote Sensing for Natural Resources, 35(3): 145-152. doi: 10.6046/zrzyyg.2022197

Citation:

LIN Jiahui, LIU Guang, FAN Jinghui, ZHAO Hongli, BAI Shibiao, PAN Hongyu. 2023. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR. Remote Sensing for Natural Resources, 35(3): 145-152. doi: 10.6046/zrzyyg.2022197

Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR

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

More Information

Corresponding author: FAN Jinghui

Received Date: 16 May 2022

Revised Date: 31 October 2022

Publish Date: 19 September 2023

Abstract

Abstract

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.
- U-Net model /
- D-InSAR /
- extraction of mining subsidence /
- semantic segmentation /
- attention module