MPG-Net: A Semantic Segmentation Model for Extracting Aquaculture Ponds in Coastal Areas from Sentinel-2 MSI and Planet SuperDove Images

Abstract

Achieving precise and swift monitoring of aquaculture ponds in coastal regions is essential for the scientific planning of spatial layouts in aquaculture zones and the advancement of ecological sustainability in coastal areas. However, because the distribution of many land types in coastal areas and the complex spectral features of remote sensing images are prone to the phenomenon of ‘same spectrum heterogeneous objects’, the current deep learning model is susceptible to background noise interference in the face of complex backgrounds, resulting in poor model generalization ability. Moreover, with the image features of aquaculture ponds of different scales, the model has limited multi-scale feature extraction ability, making it difficult to extract effective edge features. To address these issues, this work suggests a novel semantic segmentation model for aquaculture ponds called MPG-Net, which is based on an enhanced version of the U-Net model and primarily comprises two structures: MS and PGC. The MS structure integrates the Inception module and the Dilated residual module in order to enhance the model’s ability to extract the features of aquaculture ponds and effectively solve the problem of gradient disappearance in the training of the model; the PGC structure integrates the Global Context module and the Polarized Self-Attention in order to enhance the model’s ability to understand the contextual semantic information and reduce the interference of redundant information. Using Sentinel-2 and Planet images as data sources, the effectiveness of the refined method is confirmed through ablation experiments conducted on the two structures. The experimental comparison using the FCN8S, SegNet, U-Net, and DeepLabV3 classical semantic segmentation models shows that the MPG-Net model outperforms the other four models in all four precision evaluation indicators; the average values of precision, recall, IoU, and F1-Score of the two image datasets with different resolutions are 94.95%, 92.95%, 88.57%, and 93.94%, respectively. These values prove that the MPG-Net model has better robustness and generalization ability, which can reduce the interference of irrelevant information, effectively improve the extraction precision of individual aquaculture ponds, and significantly reduce the edge adhesion of aquaculture ponds in the extraction results, thereby offering new technical support for the automatic extraction of aquaculture ponds in coastal areas.