DCS-ViT: Cross-scale dynamic fusion deep hash framework for SAR and optical oil spill data retrieval

Rapid retrieval of marine oil spill data from SAR and optical remote sensing is vital for emergency response. Addressing modal confusion and insufficient cross-scale feature utilization, we propose DCS-ViT, a bimodal deep hashing framework specifically optimized for intra-modality retrieval within large-scale hybrid SAR and optical databases. It incorporates three core modules: (1) A dual-modal dedicated ViT encoder that separates and extracts features from SAR (focusing on noise suppression) and optical (focusing on detail enhancement) images; (2) a cross-scale dynamic fusion module that adaptively integrates multi-granularity features via a dynamic attention mechanism; (3) a modality-tagged hash encoding layer that explicitly distinguishes modalities while reducing parameter redundancy. We validated the framework using a new OilSpill-Multidata dataset containing 75,086 images. Results demonstrate that DCS-ViT outperforms mainstream CNN and ViT-based hashing algorithms, achieving mAP improvements across various code lengths. Furthermore, an interactive web demonstration system based on the Flask framework was developed to provide intuitive visualization of the retrieval process. Compared to traditional manual screening, DCS-ViT accelerates retrieval efficiency and accuracy, offering a robust tool for large-scale multimodal oil spill remote sensing image management.