Change detection for mine environment based on domestic high resolution satellite images
Lijuan WANG1,2,3, Xiao JIN2,3(), Hujun JIA2,3, Yao TANG2,3, Guochao MA2,3
1. College of Environment and Civil Engineering, Chengdu University of Technology, Chengdu 610045, China 2. Sichuan Academy of Safety Science and Technology, Chengdu 610045, China 3. Key Laboratory of Measurement and Control of Major Hazard Sources in Sichuan Province, Chengdu 610045, China
With the development of mine monitoring technology towards the quantification and automation, the traditional remote sensing technology based on visual interpretation is not suitable for mine monitoring. In order to improve the automation of mine remote sensing monitoring and make up for deficiencies in traditional monitoring methods, the authors constructed an object-based change detection method with high degree of automation for dynamic monitoring of mine and the surrounding environment based on GF-2 remote sensing images. The method automatically selected training samples based on change vector analysis (CVA) and extracted change information by using extreme learning machine (ELM). The experimental results show that the detection accuracy of this method is 98.73%, and it can be used in the dynamic monitoring and analysis of mine environment with highly automation. Taking the typical mine and tailings pond in Miyi County of Sichuan Province as examples, the authors carried out the dynamic monitoring of mines and the surrounding areas based on GF-2 remote sensing images. The changes of mine and its surroundings were accurately detected, which verifies the feasibility of the method and provides examples for large-scale remote sensing monitoring in mine.
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2018, Vol. 30 
), 贾虎军2,3, 唐尧2,3, 马国超2,3
