ABSTRACT Vessel re‐identification (ReID) plays a critical role in maritime surveillance by matching vessels across different camera views. Compared with person or vehicle ReID, vessel ReID faces unique challenges due to subtle interclass differences and large intraclass variations caused by viewpoint changes. These issues are further exacerbated by the highly similar appearances of vessels and the lack of fine‐grained identity cues commonly found in other ReID tasks. To address these challenges, we propose a spatial‐channel fusion network (SCF‐Net), a dual‐branch deep framework that integrates a spatial‐channel fusion (SCF) module and a feature refinement and alignment (FRA) module. The SCF module captures interdependent relationships between spatial and channel dimensions, enabling the network to emphasize discriminative regions while suppressing irrelevant background information. The FRA module refines high‐dimensional embeddings into a compact representation and enforces intraclass similarity via a learnable multilayer perceptron (MLP) and a supervised mean squared error (MSE) loss. By jointly optimizing the two branches and the FRA output, SCF‐Net effectively learns both interclass discrimination and intraclass compactness. Extensive experiments demonstrate that SCF‐Net achieves competitive performance on public vessel ReID benchmarks, highlighting its effectiveness in handling subtle interclass differences and large intraclass variations.
Lin et al. (Thu,) studied this question.