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Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

by 1,2, 1,3,4,*, 1,2, 1,3, 3 and 3
1
Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology–Beijing, Beijing 100083, China
2
School of Mechanics and Civil Engineering, China University of Mining and Technology–Beijing, Beijing 100083, China
3
School of Energy and Mining Engineering, China University of Mining and Technology–Beijing, Beijing 100083, China
4
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology–Beijing, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Amin Beiranvand Pour
Remote Sens. 2021, 13(18), 3652; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183652
Received: 18 August 2021 / Revised: 8 September 2021 / Accepted: 9 September 2021 / Published: 13 September 2021
Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 am and 5:00 am under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 am, 1:00 pm and 3:00 pm. View Full-Text
Keywords: unmanned aerial vehicle; infrared image; visible image; mining-induced ground fissure; ground fissure identification; edge detection method; fissure detection unmanned aerial vehicle; infrared image; visible image; mining-induced ground fissure; ground fissure identification; edge detection method; fissure detection
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MDPI and ACS Style

Xu, D.; Zhao, Y.; Jiang, Y.; Zhang, C.; Sun, B.; He, X. Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing. Remote Sens. 2021, 13, 3652. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183652

AMA Style

Xu D, Zhao Y, Jiang Y, Zhang C, Sun B, He X. Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing. Remote Sensing. 2021; 13(18):3652. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183652

Chicago/Turabian Style

Xu, Duo, Yixin Zhao, Yaodong Jiang, Cun Zhang, Bo Sun, and Xiang He. 2021. "Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing" Remote Sensing 13, no. 18: 3652. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183652

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