BI²Net: Graph-Based Boundary–Interior Interaction Network for Raft Aquaculture Area Extraction From Remote Sensing Images

Accurate monitoring of raft aquaculture areas (RAAs) is particularly important for the protection of marine ecosystems. However, existing semantic segmentation methods are often degraded by severe shrinkage when extracting inapparent RAAs caused by natural factors such as tide-level changes and human activities including laver harvesting. In this letter, a graph-based boundary–interior interaction network (BI2Net) is proposed for laver RAA extraction. Graph convolution based on soft clustering is introduced to capture the global distribution patterns among RAAs. For the network structure, we designed the RAA-boundary branch and RAA-interior branch, one for locating the boundaries and the other for extracting the RAAs. Importantly, unlike traditional dual-branch methods that only use additional information from the auxiliary tasks to assist with the primary task, BI2Net introduces a graph interaction module (GIM) that implements reasoning about the relationship between different distribution patterns, which sufficiently and comprehensively exploits mutual benefits between RAA extraction and boundary detection. After the addition of GIM, precision, recall, F1, and IoU were improved by 2.0%, 8.2%, 0.060%, and 8.2%, respectively. Extensive experiments have shown that our proposed BI2Net outperforms existing methods in terms of the consistency and completeness of RAA extraction, with precision, recall, F1, and IoU reaching 93.0%, 90.6%, 0.916%, and 84.7%, respectively.