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

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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.