渔业研究2025，Vol.47Issue(6)：793-802,10.DOI:10.14012/j.jfr.2025126
基于Res-PGAUnet的沿海养殖池塘遥感提取研究
Study on remote sensing extraction of coastal aquaculture ponds based on Res-PGAUnet
摘要
Abstract
[Background]The coastal aquaculture ponds are often mixed with salt fields and river channels,and the ponds have different forms and scales,which make the traditional remote sensing extraction methods face the technical bottlenecks such as insufficient extraction accuracy,weak anti-interference ability and low degree of automation.Deep learning methods,however,can automatically learn rich spectral and spatial features from images through convolutional layers,enabling large-scale precise classification and enhancing the automation of extraction tasks.[Objective]This study aimed to realize accurate and efficient automated extraction of aquaculture ponds in complex interference scenarios.[Methods]This study utilizes domestic GF-2 high-resol-ution remote sensing imagery data.Building upon the U-Net model,we constructed the Res-PGAUnet model for the coastal pond aquaculture zone in the south of Jiuzhen Bay in Zhangzhou City,Fujian Province.The mod-el integrates residual structure,pyramid pooling,guided branches,and dual attention mechanisms,with preci-sion analysis and large-scale application testing conducted.[Results]Core improvement modules(Residual structure,pyramid pooling,guided branches,and dual attention mechanism)significantly enhanced perform-ance.Their combined effect enables Res-PGAUnet to demonstrate stronger anti-interference capability and ro-bustness when handling diverse interference objects such as rivers,salt pans,and seawater.The IoU and F1-score reached 0.854 0 and 0.921 3 respectively,effectively reducing false positives and negatives while address-ing small target omissions and boundary adhesion issues.[Conclusion]Large-scale generalization tests further validate the practical potential of Res-PGAUnet.The model provides reliable technical support for precise mon-itoring of pond aquaculture spatial information and sustainable fishery development.关键词
养殖池塘/高分二号(GF-2)影像/深度学习/Res-PGAUnet/金字塔池化/引导分支/双注意力机制Key words
aquaculture pond/GF-2 imagery/deep learning/Res-PGAUnet/pyramid pooling/guide branches/dual attention mechanism分类
农业科技引用本文复制引用
陈红梅,彭俊,陈芸芝,罗冬莲,陈钰玫,刘国昕,王婉萍..基于Res-PGAUnet的沿海养殖池塘遥感提取研究[J].渔业研究,2025,47(6):793-802,10.基金项目
福建省海洋服务与渔业高质量发展专项资金项目(FJHY-YYKJ-2024-1-14、FJHY-YYKJ-2024-1-18-2) （FJHY-YYKJ-2024-1-14、FJHY-YYKJ-2024-1-18-2）
福建省水产研究所科技引领专项"基于遥感数据的海水养殖信息智能提取技术及应用研究"(2022KJYL03) （2022KJYL03）
