融合多源遥感数据和改进后Mask R-CNN深度学习模型的复杂高原地形区冰湖智能识别

张世殊; 李青春; 黎昊; 向新建; 董傲男; 窦杰

doi:10.3799/dqkx.2025.041

Volume 50 Issue 8

Aug. 2025

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Article Navigation > Earth Science > 2025 > 50(8): 3132-3143

Zhang Shishu, Li Qingchun, Li Hao, Xiang Xinjian, Dong Aonan, Dou Jie, 2025. Intelligent Glacial Lake Identification in Complex Plateau Terrain Regions Using Multi-Source Remote Sensing Data and Mask R-CNN Deep Learning Model. Earth Science, 50(8): 3132-3143. doi: 10.3799/dqkx.2025.041

Citation:

Zhang Shishu, Li Qingchun, Li Hao, Xiang Xinjian, Dong Aonan, Dou Jie, 2025. Intelligent Glacial Lake Identification in Complex Plateau Terrain Regions Using Multi-Source Remote Sensing Data and Mask R-CNN Deep Learning Model. Earth Science, 50(8): 3132-3143. doi: 10.3799/dqkx.2025.041

Citation:

Zhang Shishu, Li Qingchun, Li Hao, Xiang Xinjian, Dong Aonan, Dou Jie, 2025. Intelligent Glacial Lake Identification in Complex Plateau Terrain Regions Using Multi-Source Remote Sensing Data and Mask R-CNN Deep Learning Model. Earth Science, 50(8): 3132-3143. doi: 10.3799/dqkx.2025.041

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Intelligent Glacial Lake Identification in Complex Plateau Terrain Regions Using Multi-Source Remote Sensing Data and Mask R-CNN Deep Learning Model

doi: 10.3799/dqkx.2025.041

1.
PowerChina Chengdu Engineering Corporation Limited, Chengdu 611130, China
2.
Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China

Received Date: 2025-01-08
Publish Date: 2025-08-25

Abstract

Abstract

The identification of glacial lakes is a prerequisite for understanding their response to climate change and assessing potential risks of glacial lake outburst floods (GLOFs). Although remote sensing technology enables continuous monitoring and assessment of global glacial lake evolution, accurately and reliably extracting glacial lakes in complex plateau terrain regions remains challenging. This study proposes an intelligent glacial lake identification method for complex plateau terrain based on multi-source remote sensing data and an improved Mask R-CNN deep learning model. Building upon the original Mask R-CNN framework, we introduce attention mechanisms at three key components: the high-level features (Conv4 and Conv5) of the ResNet-50 backbone network, each feature map in the Feature Pyramid Network (FPN), and the Mask Head. Utilizing a multi-band dataset composed of Sentinel-2 high-resolution imagery, ALOS-DEM, and Normalized Difference Water Index (NDWI) data, we conducted tests in Nyingchi City, southeastern Tibetan Plateau. Comparative analyses were performed between the enhanced Mask R-CNN model and three other models (U-Net, SegNet, and DeepLab V3) for glacial lake identification. Results demonstrate that the improved Mask R-CNN achieves superior accuracy, with precision, recall, and accuracy values reaching 91.25%, 93.69%, and 92.89% respectively. The enhanced model effectively mitigates interference from mountain shadows, lake turbidity, and freeze-thaw conditions on glacial lake identification while significantly improving detection efficiency for small glacial lakes. This research provides a reliable solution for glacial lake identification in complex plateau terrain regions and establishes a novel framework combining deep learning with multi-source remote sensing data for intelligent glacial lake extraction, offering new possibilities for related studies.
- Glacial lake,
- complex plateau terrain,
- intelligent recognition,
- mask R-CNN model,
- source remote sensing data

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References

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Intelligent Glacial Lake Identification in Complex Plateau Terrain Regions Using Multi-Source Remote Sensing Data and Mask R-CNN Deep Learning Model

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