SAR to Optical Image Translation with Color Supervised Diffusion Model
This work addresses the problem of SAR image interpretation for remote sensing and analysis applications, representing an incremental improvement by integrating color supervision into an existing diffusion model framework.
The paper tackles the challenge of interpreting Synthetic Aperture Radar (SAR) images by developing a generative model that translates them into more intelligible optical images, resulting in superior performance in quantitative metrics like peak signal-to-noise ratio and structural similarity, and improved visual quality compared to previous methods.
Synthetic Aperture Radar (SAR) offers all-weather, high-resolution imaging capabilities, but its complex imaging mechanism often poses challenges for interpretation. In response to these limitations, this paper introduces an innovative generative model designed to transform SAR images into more intelligible optical images, thereby enhancing the interpretability of SAR images. Specifically, our model backbone is based on the recent diffusion models, which have powerful generative capabilities. We employ SAR images as conditional guides in the sampling process and integrate color supervision to counteract color shift issues effectively. We conducted experiments on the SEN12 dataset and employed quantitative evaluations using peak signal-to-noise ratio, structural similarity, and fréchet inception distance. The results demonstrate that our model not only surpasses previous methods in quantitative assessments but also significantly enhances the visual quality of the generated images.