Fine-scale information extraction of water bodies in the Erhai Lake Basin based on an improved DeepLabV3plus architecture

doi:10.6046/zrzyyg.2024374

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Abstract

Traditional methods for information extraction of small water bodies suffer from poor performance and low accuracy, failing to meet actual needs. Using the high-resolution images of the Erhai Lake basin from the Jilin-1 domestic satellite as the data source, this study proposed a deep learning-based semantic segmentation method using an improved DeepLabV3plus model. Replacing the ResNet-101 encoder with EfficientNet-B4, this study innovatively combined the BCE Loss and Dice Loss functions, identifying the optimal method for fine-scale information extraction of water bodies in the Erhai Lake Basin. The results indicate that compared to traditional methods, the improved DeepLabV3plus model performed better in the information extraction of water boundaries, enabling accurate identification of main water bodies, especially small streams. The improved DeepLabV3plus model exhibited higher precision (98.87%), recall (99.30%), and F1-Score (99.08%) than the normalized difference water index (NDWI) and object-oriented methods. Regarding comparison of details, the improved DeepLabV3plus model can effectively suppress the influence of building shadows, vegetation occlusion, and complex surface features, improving the information extraction effects of small water bodies and complex edge areas. In addition, ablation experiments show that the introduction of the combined loss functions and compound scaling strategy increased mIoU by 0.62% and 3.07%, respectively, significantly enhancing the model's segmentation accuracy and ability to extract multi-scale semantic information.

Keywords improved DeepLabV3plus high-resolution remote sensing image semantic segmentation Erhai Lake Basin water body information extraction

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Issue Date: 31 December 2025

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	Ying ZHANG
	Yunchun CHEN
	Xiaofei GUO
	Xiaocong WU
	Fenglin CHEN
	Weijun ZENG

Cite this article:

Ying ZHANG,Yunchun CHEN,Xiaofei GUO, et al. Fine-scale information extraction of water bodies in the Erhai Lake Basin based on an improved DeepLabV3plus architecture[J]. Remote Sensing for Natural Resources, 2025, 37(6): 201-210.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024374 OR https://www.gtzyyg.com/EN/Y2025/V37/I6/201

Fig.1 Geographic location map of the study area

Tab.1 Imaging parameters of the Jilin-1 satellite series

Fig.2 Jilin-1 GF02F satellite imagery

Fig.3 Segmentation results at the optimal scale

Fig.4 The structure of improved DeepLabV3plus model

Fig.5 Extraction results of the northern Erhai Lake region

Fig.6 Extraction results of the western Erhai Lake region

Tab.2 Comparison of water body extraction accuracy(%)

Tab.3 Detailed results of water body extraction

Tab.4 Comparison of ablation experiment results(%)

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