SeaMamba: Frequency-Stabilized Selective State-Space Multiscale Detection for SAR Ships in Complex Maritime Scenes

Abstract

Ship detection in synthetic aperture radar (SAR) imagery remains challenging because near-shore clutter, coherent speckle noise, dense scattering responses, and large target-scale variations often obscure vessel boundaries and weaken small-ship signatures. Although single-stage detectors provide efficient inference, their predominantly local convolutional modeling and fixed multiscale fusion strategies are insufficient for capturing long-range sea-surface context and adaptively emphasizing discriminative ship responses. To address these limitations, this paper proposes SeaMamba, a frequency-stabilized selective state-space multiscale detector for SAR ship detection in complex maritime scenes. Specifically, a frequency-domain speckle prior is introduced to stabilize SAR inputs while preserving target localization cues. A bidirectional selective state-space modeling module is then used to propagate long-range contextual information with input-adaptive scanning. Furthermore, a gated pyramid reassembly module is designed to refine multiscale features before dense prediction. The proposed method is evaluated on the SAR Ship Detection Dataset (SSDD) and High-Resolution SAR Images Dataset (HRSID) under a unified five-fold cross-validation protocol. SeaMamba achieved mean average precision at an intersection-over-union threshold of 0.5 (mAP@0.5) values of 99.16 ± 0.11% on SSDD and 93.74 ± 0.15% on HRSID. Per-category evaluation, ablation studies, efficiency analysis, and Grad-CAM-based interpretability visualization further demonstrate that SeaMamba improves small-vessel detection, suppresses near-shore false responses, and maintains a practical accuracy-efficiency trade-off.