Annotation-Free, High-Fidelity SAR Oil-Spill Image Synthesis via Classification-Guided Diffusion Model

Synthetic aperture radar (SAR) imagery is indispensable for rapid, weather-independent marine oil-spill monitoring. Yet, the acute shortage of annotated SAR spill imagery severely limits deep learning detectors. While generative adversarial networks (GANs) have been used to generate synthetic data, their inherent limitations—training instability and mode collapse—often result in blurred and semantically inconsistent outputs. To overcome these challenges, we introduce a classification-guided diffusion model (CG-DM), which integrates the expressive power of diffusion processes with task-specific guidance. CG-DM incorporates three key innovations: 1) morphology-aware classification guidance: SAR oil-spill images are categorized into different morphological categories (blocky, elongated, and patchy); this category information conditions every step of the reverse-diffusion process, enabling fine-grained control over the global geometry of generated spills while preserving intracategory diversity; 2) label-synchronized generation: the model simultaneously generates the SAR image and its corresponding pixel-level annotation mask, which eliminates the need for the time-consuming and error-prone manual labeling process; and 3) spatial-aware attention mechanism: a lightweight attention mechanism performs localized window self-attention with relative positional offsets, which significantly enhances the sharpness of spill edges and the fidelity of speckle-textured details. Evaluated on the M4D benchmark semantic segmentation dataset of the Information Technologies Institute (ITI)/European Maritime Safety Agency (EMSA) with Sentinel-1 SAR imagery from 2015 to 2017, CG-DM achieves a Fréchet inception distance (FID) of 203.14 and a kernel inception distance (KID) of 0.124, surpassing state-of-the-art GAN baselines by substantial margins. Besides, ablation studies confirm the critical contributions of each component: spatial-aware attention mechanism significantly enhances generation quality, while classification guidance effectively preserves morphological feature diversity. Crucially, under extreme data scarcity, training augmentation with CG-DM synthetic samples improves oil-spill detection IoU by up to 81%, demonstrating strong practical utility. This study establishes the first annotation-free paradigm for SAR oil-spill data generation, paving the way for high-accuracy maritime disaster monitoring systems.