Coal Exploration Based on a Multilayer Extreme Learning Machine and Satellite Images

IEEE Access

Dakuo He¹  Ba Tuan Le¹  Dong Xiao¹  Shengyong Zhang¹  Yachun Mao²  Xiaoyu Sun²  Xiaobo Liu² 

[1] Information Science and Engineering School, Northeastern University, Shenyang, China;Intelligent Mine Research Center, Northeastern University, Shenyang, China;

关键词: Neural networks;    remote sensing;    satellites;    sensors;   

DOI  :  10.1109/ACCESS.2018.2860278

来源: DOAJ

【 摘 要 】

The demand for coal has been on the rise in modern society. With the number of opencast coal mines decreasing, it has become increasingly difficult to find coal. Low efficiencies and high casualty rates have always been problems in the process of coal exploration due to complicated geological structures in coal mining areas. Therefore, we propose a new exploration technology for coal that uses satellite images to explore and monitor opencast coal mining areas. First, we collected bituminous coal and lignite from the Shenhua opencast coal mine in China in addition to non-coal objects, including sandstones, soils, shales, marls, vegetation, coal gangues, water, and buildings. Second, we measured the spectral data of these objects through a spectrometer. Third, we proposed a multilayer extreme learning machine algorithm and constructed a coal classification model based on that algorithm and the spectral data. The model can assist in the classification of bituminous coal, lignite, and non-coal objects. Fourth, we collected Landsat 8 satellite images for the coal mining areas. We divided the image of the coal mine using the constructed model and correctly described the distributions of bituminous coal and lignite. Compared with the traditional coal exploration method, our method manifested an unparalleled advantage and application value in terms of its economy, speed, and accuracy.

【 授权许可】

Unknown