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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 145-152     DOI: 10.6046/zrzyyg.2022197
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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
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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.
Keywords U-Net model      D-InSAR      extraction of mining subsidence      semantic segmentation      attention module     
ZTFLH:  TP79  
Issue Date: 19 September 2023
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Jiahui LIN
Guang LIU
Jinghui FAN
Hongli ZHAO
Shibiao BAI
Hongyu PAN
Cite this article:   
Jiahui LIN,Guang LIU,Jinghui FAN, et al. Extracting information about mining subsidence by combining an improved U-Net model and D-InSAR[J]. Remote Sensing for Natural Resources, 2023, 35(3): 145-152.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022197     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/145
Fig.1  Geographical location of the study area
Fig.2  Technical flow chart
Path113_Frame116 Path113_Frame121
20191116,20191128 20191011,20191104
20191116,20191210 20191104,20191128
20191128,20191210 20191116,20191128
20191128,20191222 20191128,20191210
20191210,20191222 20191210,20191222
Tab.1  Differential interference image pairs used in the experiment
样本1 样本2 样本3
相位图
标记图
Tab.2  Mining subsidence phase diagram and labeled diagram
Fig.3  ECA-UNet model structure
模型 PA/% MIoU
/%		 IoU
(背景)
/%		 IoU
(采矿
沉陷)/%		 训练
时间/h
FCN-32s 97.71 73.68 98.21 49.15 1.84
FCN-16s 98.16 76.61 98.38 54.84 1.88
FCN-8s 98.29 78.19 98.51 57.87 2.11
PSPNet 98.16 79.21 98.65 59.77 6.50
Deeplabv3 98.20 79.71 98.57 60.85 7.50
U-Net 98.31 79.24 98.27 60.20 5.08
本文模型 98.55 80.58 98.41 62.74 6.36
Tab.3  Accuracy evaluation of different models on test set
场景 相位图 标记图 FCN-8s PSPNet Deeplabv3 U-Net 本文模型
场景1
场景2
场景3
Tab.4  Results of different models
Fig.4  Mining subsidence phase diagram and extraction result
Fig.5  Water bodies wrongly divided into mining subsidence areas
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