Triple-Level Topology Awareness Using Hypergraph for Marine Ship Surveillance from SAR Imagery

Marine ship surveillance from synthetic aperture radar (SAR) imagery is extensively studied. Still, previous methods may not fully capture high-order topological relationships, which can limit performance in complex scenarios. To this end, we propose a triple-level topology awareness (TLTA) framework using hypergraphs for effective SAR marine ship surveillance. TLTA dynamically captures higher-order dependencies in latent spaces by hypergraph convolution, addressing a limitation of prior techniques that rely solely on pairwise correlation analysis. TLTA is implemented at three levels—input-level, feature-level, and proposal-level—to obtain gradually enhanced feature representations, known as i-LTA, f-LTA, and p-LTA. i-LTA designs a super-pixel segmentation module (SPSM) to yield compact and semantically similar regions through an efficient iterative clustering, and the resulting regions are used for super-pixel hypergraph construction (SP-HGC) to produce features rich in spatial topology relationships at the input level, and finally, a cross-attention collaborative network (CACN) is constructed to aggregate high-order and low-order features to achieve a synergistic integration of topological structures and key details. f-LTA explores topology awareness in the backbone feature extraction, mainly through feature-adaptive hypergraph construction (FA-HGC), vertex-level feature self-attention (VL-FSA), and edge-level feature self-attention (EL-FSA), to enable comprehensive modeling of complex inter-patch dependencies beyond simplistic pairwise interactions. p-LTA leverages a proposal prediction network (PPN) to yield positive/negative proposals for proposal-guided hypergraph construction (PG-HGC), and then leverages instance-level spatial priors to ensure the topology interactions between feature subsets. Experimental results reveal the competitive performance of TLTA, achieving AP values of 77.9% and 76.4% on SSDD and HRSID, respectively, and demonstrate the efficacy of each strategy.