计算机应用与软件2026，Vol.43Issue(2)：164-174,188,12.DOI:10.3969/j.issn.1000-386x.2026.02.022
基于改进卷积神经网络的水体分割方法
WATER SEGMENTATION METHOD BASED ON IMPROVED CONVOLUTIONAL NEURAL NETWORK
摘要
Abstract
Due to the complex multi-scale characteristics of water in remote sensing images,traditional methods are prone to misjudgment and omission during water extraction.To address this issue,a new network structure that integrates local and global information is proposed.The network designed a residual module with an attention mechanism at the encoder end to capture both global and local information for each positional feature,and employed multipath dilated convolution to achieve multi-scale water feature extraction.To improve segmentation accuracy at water boundaries,a refined attention fusion module was designed at the decoder end of the network.Experimental results show that the network achieves recall,precision,and F1-scores of 95.78%,94.24%and 93.75%,respectively.Compared with traditional convolutional neural networks,these evaluation metrics are improved by 1.56,1.72,and 1.62 percentage points,respectively.关键词
水体分割/全局注意力机制/多路径扩张卷积/局部和全局信息Key words
Water segmentation/Global attention module/Multipath dilated convolution/Local and global information分类
信息技术与安全科学引用本文复制引用
张永宏,孙岩,田伟,马光义,朱灵龙..基于改进卷积神经网络的水体分割方法[J].计算机应用与软件,2026,43(2):164-174,188,12.基金项目
国家自然科学基金面上项目(41875027). （41875027）
