PhDGAN: A Physics-Informed Dual-Branch GAN With Gamma-Prior for Dual-Polarization SAR Image Colorization

Abstract

Synthetic aperture radar (SAR) has become an indispensable tool in Earth observation due to its capability for all-weather and day-and-night data acquisition. However, unlike optical sensors, the inherent coherent imaging mechanism of SAR results in single-channel grayscale images lacking intuitive spectral information, which poses significant challenges for efficient land cover discrimination and visual interpretation. To bridge the gap between SAR physical mechanisms and human visual perception, this article proposes a physics-informed dual-branch generative adversarial network (PhDGAN) for the simultaneous colorization of dual-polarization (VV/VH) SAR images. Unlike existing methods that treat colorization as a generic image-to-image translation (I2IT) task, our approach explicitly incorporates physical scattering properties. Central to the architecture is the Gamma-prior embedding module (GPEM), which utilizes the Gamma distribution to model backscattering statistics. By extracting scale and shape parameters representing terrain physical attributes, GPEM guides the generator to synthesize physically plausible color features. Furthermore, we design a dual-branch generator constrained by a coupled supervision loss, which enforces consistency between polarization channels by leveraging target image differences. Addressing the scarcity of ground-truth data, we introduce a spectral correction module (SCM)-based label generation method and construct a large-scale dual-polarization SAR colorization dataset (DPSCD). Extensive experiments demonstrate that PhDGAN outperforms state-of-the-art (SOTA) techniques, offering a robust solution for enhancing SAR image interpretability. The datasets and codes are available at https://github.com/NUAA-RS/PhDGAN