Physics-Guided cGAN with Multi-Scale Attention for Robust 2D Phase Unwrapping in Low-Coherence Regions

Two-dimensional (2D) phase unwrapping (PU) in interferometric synthetic aperture radar (InSAR) remains a challenging ill-posed inverse problem, particularly in low-coherence regions where the Itoh condition is violated. Although deep-learning methods have shown promise, existing networks often struggle to preserve phase continuity and structural details under strong decorrelation noise. To address these limitations, a novel physics-guided conditional generative adversarial network (cGAN) is proposed in this work. Specifically, an adaptive atrous spatial pyramid pooling module mimics the multi-baseline observation mechanism to resolve multi-scale phase variations. Meanwhile, a multi-channel attention-augmented discriminator guides adversarial learning to focus on error-prone low-coherence regions. Additionally, a hybrid loss function enforces fundamental physical constraints, including phase-wrapping consistency and path continuity, explicitly embedding the prior knowledge of interferometric phase physics into the data-driven framework. Extensive experiments demonstrate that the proposed method achieves highly competitive performance in terms of root mean square error (RMSE) and structural similarity (SSIM) compared to State-of-the-Art approaches, especially in scenarios with severe noise and discontinuities.