SAR-DisentDM: A Semantic-Disentangled Diffusion Model for Limited-Data SAR Image Synthesis

Synthetic aperture radar (SAR) image acquisition incurs high costs, motivating SAR image generation research under limited data. However, SAR's inherent azimuth sensitivity complicates this task, where target scattering characteristics vary significantly with azimuth angle, leading to azimuth-target coupling and azimuth overfitting under data scarcity. Most existing methods require supplementary data to work effectively, limiting their practicality. In this paper, we propose SAR-DisentDM, a novel semantic-disentangled diffusion model for limited-data SAR image generation, without requiring any auxiliary resources. We develop a physics-aware diffusion architecture that explicitly models semantic knowledge of SAR images, including intrinsic characteristics, contextual diversity, and measurement randomness. A key innovation is the attention-guided semantic disentanglement module (AGSD), designed to decouple category-specific features from azimuth-variable scattering patterns using dual disentangled losses with time-step-adaptive optimization. To further avoid azimuth overfitting, we introduce azimuth angle perturbation augmentation mechanism (AAPA) to enhance azimuth angle diversity. Extensive evaluations validate that SAR-DisentDM enables controllable SAR image synthesis with designated attributes, significantly improving representation and generalization abilities under limited data. Synthetic imagery from our approach boosts automatic target recognition (ATR) accuracy beyond state-of-the-art methods.