STDPNet: supervised transformer-driven network for high-precision oil spill segmentation in SAR imagery

Abstract

Oil spill incidents are one of the major factors damaging marine ecosystems, and there is an urgent need for effective detection and identification technologies to quickly locate oil spill contamination areas. Synthetic Aperture Radar (SAR) is capable of monitoring the ocean surface under various weather and lighting conditions, but the SAR images often contain dense speckle noise, and popular SAR oil spill image datasets typically lack sufficient polarization information. To overcome these issues, this study introduces a novel polarimetric decomposition method to generate synthetic color image datasets that integrate multiple polarization features, thereby enhancing image texture and contrast. An image denoising module is designed, which reduces noise interference in the color images through an adaptive sampling approach. Furthermore, a novel Transformer-CNN architecture model is proposed, integrating two modules: the Super Visual Attention Transformer and the Directional Multi-Branch Scale Self-Calibration Module. The segmentation performance of the model is comprehensively evaluated on three datasets, and compared with state-of-the-art segmentation methods, demonstrating superior classification accuracy and stability. This research provides an effective technical support for accurate oil spill detection and marine ecosystem protection.