MSARG-Net: A Multimodal Offshore Floating Raft Aquaculture Area Extraction Network for Remote Sensing Images Based on Multiscale SAR Guidance

Abstract

Accurately extracting offshore floating raft aquaculture (FRA) areas from remotely sensed images is the key to rationally managing aquaculture resources. Currently, deep learning-based methods perform well in FRA area extraction tasks but are limited by the shortcomings of single-modality remote sensing data, which affect their extraction accuracies. To solve this problem, we constructed a multimodal dataset called CHN-YS3-FRA for FRA area extraction using heterogeneous Sentinel-1/-2 remote sensing image data and proposed a new multiscale synthetic aperture radar (SAR) guidance network (MSARG-Net) for performing FRA area extraction on multimodal remote sensing images. In this network, we designed a global-local Poolformer block to model the local and global relationships of FRA areas to more comprehensively learn the semantic features of these areas. In addition, we designed a multiscale SAR-guided attention block to efficiently fuse the semantic information acquired from different modalities. The experimental results obtained on the CHN-YS3-FRA dataset show that MSARG-Net could robustly extract offshore FRA regions with F1 scores, intersection-over-union and kappa coefficient values of 91.46%, 84.26%, and 89.69%, respectively. Compared with the latest remote sensing-based semantic segmentation methods, MSARG-Net has achieved significant quantitative and qualitative improvements and has significant potential for mapping and monitoring large-scale offshore FRA areas.