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Opencast coal mine scene recognition based on sub-region multi-label learning

  • role： First author第一作者
  • Affiliation：

    School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

    Key Laboratory of Degraded and Unused Land Consolidation Engineering, Ministry of Natural Resources, Xi'an 710075, China

  • Email：zhaoyd@cumt.edu.cn
  • Introduction：赵银娣，研究方向为遥感数据处理、模式识别。E-mail：zhaoyd@cumt.edu.cn
ZHAO Yindi12,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

  • Email：925074644@qq.com
  • Introduction：卫虹宇，研究方向为遥感目标检测、场景识别。E-mail：925074644@qq.com
WEI Hongyu1*,  
  • Affiliation：

    School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

DONG Jihong1,  
  • Affiliation：

    School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China

DONG Chang1

Resümee

Opencast coal mining activities would lead to negative impacts on regional ecological environment, ensuring efficient monitoring and regulation of mining activities would promote environmental protection and sustainable development. With the development of remote sensing technology and artificial intelligence, there is great potential in automatically detecting opencast coal mine areas from high spatial resolution remote sensing imagery. Aiming at the problem of low recognition rate of scene sub-region recognition via single-label learning algorithm, this paper proposes an opencast coal mine scene recognition method by integrating multi-label learning and the first law of geography. In order to distinguish the opencast coal mine scenes from their surrounding different scenes, six categories of mining labels and seven categories of non-mining labels are set, and 9768 sub-region images are annotated to create a multi-label dataset. The Inception-v3 model is trained using the created dataset to perform multi-label classification. For scene recognition, firstly, the remote sensing image covering the study area is divided into non-overlapping sub-regions of the same size and the multi-label classification is carried out on the divided sub-regions. Then, inspired by the first law of geography, the sub-regions containing the mining labels are assigned to the coal mine scene type or not according to the correlation between the labels and the completeness of the labels. Finally, all the sub-regions judged as the coal mine scene type constitute the opencast coal mine scene recognition result from the high spatial resolution remote sensing image covering the study area. The experimental results show that the recognition result of Shengli west opencast coal mine areas obtained by the proposed method is much closer to the ground truth than the comparative methods based on single-label learning. The F1 score of the proposed method reaches 0.857 in the multi-label classification, with an increase of 8 percentage points compared to the ResNet50 single-label learning method which has the best performance in the compared single-label learning methods. The proposed method can automatically extract the effective features of multiple labels in sub-regions and improve the performance of opencast coal mine scene recognition, its recognition results can provide data support for opencast mining management.

Schlüsselwort

high-resolution remote sensing image;opencast coal mine scene recognition;Multi-label learning;Scene sub-region recognition

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