Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images

ZHANG Chengye， XING Jianghe; LI Jun， SANG Xiao; ${author.authorNameEn}; ${author.authorNameEn}

doi:10.6046/zrzyyg.2021017

2021 Vol. 33, No. 4

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ZHANG Chengye， XING Jianghe, LI Jun， SANG Xiao, . 2021. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images. Remote Sensing for Natural Resources, 33(4): 252-257. doi: 10.6046/zrzyyg.2021017

Citation:

ZHANG Chengye， XING Jianghe, LI Jun， SANG Xiao, . 2021. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images. Remote Sensing for Natural Resources, 33(4): 252-257. doi: 10.6046/zrzyyg.2021017

Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images

1. College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China；
2. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology-Beijing, Beijing 100083, China

Received Date: 14 January 2021

Publish Date: 15 December 2021

Abstract

Abstract

It is of great significance for the monitoring and supervision of tailing ponds in China to realize the rapid recognition of the spatial scopes of tailing ponds using the remote sensing technique. Based on the U-Net framework, this paper proposes a deep learning-based intelligent recognition method of the spatial ranges of tailing ponds using the remote sensing technique. The method proposed was verified in Honghe Hani and Yi Autonomous Prefecture in Yunnan Province using Chinese GF-6 satellite images. The results show that the precision, recall rate, and F1-score of the method were 0.874, 0.843, and 0.858, respectively, which were significantly better than those obtained using the methods of random forest, support vector machine, and maximum likelihood. Furthermore, the time consumption of the new method kept the same order of magnitude as that of the three methods. Therefore, the method proposed in this study has a broad application prospect in the rapid monitoring of the spatial scopes of tailing ponds in China.
- deep neural network /
- GF-6 satellite /
- tailing pond /
- recognition based on remote sensing images /

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References

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