Water extraction from Sentinel-1 images based on improved DeepLabV3+ network

doi:10.13474/j.cnki.11-2246.2025.0311

Abstract

Abstract: In order to improve the accuracy of water extraction from radar images, this paper uses Sentinel-1 series images from 2023 as the data source, optimizes the backbone network on the basis of the DeepLabV3+ network model, integrates the SE channel attention mechanism, and proposes an improved deep learning network model SEDeepLabV3+. The ablation experiment is carried out for the improved model, and the model is verified by the water body extraction in Changping district of Beijing on July 31. The experimental results show that when the improved SEDeepLabV3+ method is used to extract water body, the mean intersection over union and pixel accuracy can reach 88.55% and 93.49%. Compared with DeepLabV3+, HRNet and U-Net, the average intersection ratio is increased by 2.26%,2.31% and 5.08%,and the average pixel accuracy is increased by 0.76%,0.80% and 3.07%,respectively. The improved SEDeepLabV3+ not only has a lighter network structure,but also can effectively improve the accuracy and efficiency of water extraction.

Key words: DeepLabV3+, water extraction, SE channel attention mechanism, Sentinel-1 images, semantic segmentation

CLC Number:

P237

ZHAO Xingwang, ZHAO Yan, LIU Chao, LIU Chunyang. Water extraction from Sentinel-1 images based on improved DeepLabV3+ network[J]. Bulletin of Surveying and Mapping, 2025, 0(3): 66-70.

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