WMoE-Net: wavelet-directed cross-attention alignment with mixture-of-experts network for multimodal SAR-optical ship detection

Multisource remote-sensing fusion benefits port and near-shore ship surveillance: optical images provide rich textures in daylight, while SAR remains informative in day-and-night and all-weather conditions. Yet high-resolution SAR–optical ship detection is challenged by scarce near-simultaneous acquisitions, residual misalignment after co-registration, and condition-dependent modality degradation. We curate MSOSD-1.0, a high-resolution paired SAR–optical dataset for port/near-shore ship detection, containing 1,715 co-registered patch pairs with 17,915 oriented ship instances and diverse conditions (low illumination, clouds/fog, cluttered backgrounds). On this basis, we propose WMoE-Net, a correspondence-constrained and reliability-aware alignment–gating–fusion framework. A wavelet-directed cross-attention module (WCAM) enhances local correspondence under residual mismatch. A condition-informed quality gating network (CIQGN) performs sparse expert routing among optical-preferred, SAR-preferred, and collaborative-fusion branches, while a mutually-guided consistency-aware fusion expert (MCFM) refines the collaborative branch to enhance fusion quality. Experiments on MSOSD-1.0 and QXS-SAROPT-SHIP show consistent gains over strong single-modal and multimodal baselines, especially under low illumination and clouds/fog.