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基于信息传输方式重构U型水体提取网络研究

曹凯 刘瑞 周棋

物探化探计算技术2026,Vol.48Issue(3):386-394,9.
物探化探计算技术2026,Vol.48Issue(3):386-394,9.DOI:10.12474/wthtjs.20250423-0002

基于信息传输方式重构U型水体提取网络研究

Research on U-shaped water body extraction network based on reconfiguration of information transmission mode

曹凯 1刘瑞 2周棋2

作者信息

  • 1. 成都理工大学 地球物理学院,成都 610059||四川省金属地质调查研究所,成都 611730
  • 2. 成都理工大学 地球物理学院,成都 610059
  • 折叠

摘要

Abstract

Water body extraction from remote sensing images,as an efficient means of water body monitoring,has received widespread attention with the continuous development of deep learning technology in remote sensing.However,challenges such as multi-scale Water body extraction from remote sensing images,as an efficient means of water body monitoring,has received widespread attention with the continuous development of deep learning technology in remote sensing.However,challenges such as multi-scale feature extraction,visual noise interference,and the accurate identification of fuzzy boundaries remain,limiting the performance improvement of existing models.To address these challenges,this paper proposes an improved deep learning model,RASP-Unetplus,based on the classical U-Net architecture and incorporating a residual structure,empty space pyramid pooling(ASPP),and an attention mechanism.By introducing the idea of residual learning,the common problems of gradient vanishing and gradient explosion in deep networks are effectively mitigated;at the same time,the combination of ASPP and the attention mechanism not only highlights the information of important features but also significantly enlarges the sensory field of the model,which effectively solves the problems of multi-scale feature extraction and boundary ambiguity.The experimental results show that,compared with a variety of classical state-of-the-art models,RASP-Unetplus achieves the best comprehensive performance,with an intersection-over-union(IoU)of 94.92%,fully demonstrating the model's effectiveness and superiority in remote sensing imagery water body extraction tasks.

关键词

水体提取/卷积神经网络/注意力机制/残差/空洞空间金字塔池化/U-Net

Key words

water body extraction/convolutional neural network/attention mechanism/residuals/null space pyramid pooling/U-Net

分类

信息技术与安全科学

引用本文复制引用

曹凯,刘瑞,周棋..基于信息传输方式重构U型水体提取网络研究[J].物探化探计算技术,2026,48(3):386-394,9.

基金项目

地质灾害防治与地质环境保护国家重点实验室项目(SKLGP2022K026) (SKLGP2022K026)

物探化探计算技术

1001-1749

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