A Deep Learning Method for Offshore Raft Aquaculture Extraction Based on Medium-Resolution Remote Sensing Images

Abstract

Aquaculture has experienced significant growth, contributing to resolving the global food crisis and delivering substantial economic benefits. Nevertheless, the uncontrolled expansion of aquaculture activities has led to an ecological crisis in offshore waters. This highlights the critical need for precise delineation and monitoring of aquaculture areas in these regions to ensure scientific management and sustainable development of coastal areas. In this article, we introduced an SRUNet model based on the Swin Transformer for accurately extracting offshore raft aquaculture areas using medium-resolution remote sensing images. Our SRUNet model combined the UNet model with the Swin Transformer block and the residual block to account for multiscale features, resulting in excellent extraction performance in diverse and complex sea areas. To evaluate the model, we selected four typical raft aquaculture areas and compared the SRUNet model with other comparative network models. Results revealed that the SRUNet model outperformed all other models, and the F1 Score and MIoU of the classification results were 86.52% and 87.22%, respectively. The model reduced the loss of feature information and misclassification of aquaculture areas, generating extraction effects that aligned closely with real aquaculture area shapes. Additionally, we tested the performance of each component of the SRUNet model. The results indicate that the SRUNet model exhibits strong robustness and effectively filters out irrelevant information. These results demonstrate the model's potential for large-scale extraction of offshore aquaculture areas.

Keywords

• Raft aquaculture
• residual block
• sentinel series satellites data
• Swin Transformer