Information extraction of coal gangue mountain based on random forest algorithm

FAN Yinglin; DU Song; ZHAO Yue; QIU Jingzhi; DU Xiaochuan; ZHANG Yufeng; DING Yan; SONG Sitong; CHE Qiaohui

doi:10.6046/zrzyyg.2023231

2025 Vol. 37, No. 1

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Article navigation > Remote Sensing for Natural Resources > 2025 > 37(1): 54-61

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FAN Yinglin, DU Song, ZHAO Yue, QIU Jingzhi, DU Xiaochuan, ZHANG Yufeng, DING Yan, SONG Sitong, CHE Qiaohui. 2025. Information extraction of coal gangue mountain based on random forest algorithm. Remote Sensing for Natural Resources, 37(1): 54-61. doi: 10.6046/zrzyyg.2023231

Citation:

FAN Yinglin, DU Song, ZHAO Yue, QIU Jingzhi, DU Xiaochuan, ZHANG Yufeng, DING Yan, SONG Sitong, CHE Qiaohui. 2025. Information extraction of coal gangue mountain based on random forest algorithm. Remote Sensing for Natural Resources, 37(1): 54-61. doi: 10.6046/zrzyyg.2023231

Information extraction of coal gangue mountain based on random forest algorithm

1. General Prospecting Institute of China National Administration of Coal Geology, Institute of Geological Deep Well Injection and Stroage, Beijing 100039, China;
2. China National Administration of Coal Geology, Beijing 100038, China;
3. China Mining Association, Beijing 100029, China;
4. Suzhou Industry Park Mapping Co., Ltd., Suzhou 215000, China

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Corresponding author: DU Song

Received Date: 24 July 2023

Revised Date: 24 October 2023

Abstract

Abstract

Coal gangue mountains are key areas for the ecological restoration of coal mines. Understanding their geographical distribution holds great significance for regional environmental management. This study focused on part of Xinluo District, Longyan City, Fujian Province. Using GF-2 remote sensing images and data from the ASTER GDEM digital elevation model, this study extracted spectral, texture, and topographic features and then optimized these features using the sequential forward selection method. Subsequently, this study developed a model for surface feature classification using a random forest algorithm. Using this model, this study categorized surface features by integrating multi-source data and comprehensive feature combinations and then achieved the identification and information extraction of coal gangue mountains. The results indicate that the classification accuracy did not necessarily increase with the number of features. After feature selection, the number of features was reduced from 17 to 9, and the overall extraction accuracy of coal gangue mountains reached 94.07%, with a Kappa coefficient of 0.819. Factors playing an important role in the identification and information extraction of coal gangue deposit areas included elevation, slope, aspect, multi-spectral bands B1, B2, and B4 in the spectral characteristics, normalized vegetation index, and grayscale value of images. In contrast, texture features merely improved the accuracy of surface feature types with distinct textural variations, while producing limited effects on the information extraction of coal gangue mountains. For the study area, only the mean texture feature produced significant effects on the information extraction accuracy of coal gangue mountains. The combination of random forest and feature optimization algorithm can effectively enhance the information extraction accuracy of coal gangue mountain, efficiently integrate multi-source feature data, and accelerate model calculation, serving as a practically feasible method for the information extraction of coal gangue mountains.
- remote sensing /
- GF-2 image /
- random forest classification /
- coal gangue /
- feature optimization

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