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-resolution 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 model integrates residual structure, pyramid pooling, guided branches, and dual attention mechanisms, with precision analysis and large-scale application testing conducted.
Results Core improvement modules (Residual structure, pyramid pooling, guided branches, and dual attention mechanism) significantly enhanced performance. Their combined effect enables Res-PGAUnet to demonstrate stronger anti-interference capability and robustness when handling diverse interference objects such as rivers, salt pans, and seawater. The IoU and F1-score reached 0.8540 and 0.9213 respectively, effectively reducing false positives and negatives while addressing 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 monitoring of pond aquaculture spatial information and sustainable fishery development.
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