Multisensor Diffusion-Driven Optical Image Translation for Large-Scale Applications

Abstract

Comparing images captured by disparate sensors is a common challenge in remote sensing. This requires image translation—converting imagery from one sensor domain to another while preserving the original content. Denoising diffusion implicit models (DDIM) are potential state-of-the-art solutions for such domain translation due to their proven superiority in multiple image-to-image translation tasks in computer vision. However, these models struggle with reproducing radiometric features of large-scale multipatch imagery, resulting in inconsistencies across the full image. This renders downstream tasks like heterogeneous change detection impractical. To overcome these limitations, we propose a method that leverages denoising diffusion for effective multisensor optical image translation over large areas. Our approach super-resolves large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors, ensuring uniformity across hundreds of patches. Our contributions lie in new forward and reverse diffusion processes that address the challenges of large-scale image translation. Extensive experiments using paired Sentinel-II (10 m) and Planet Dove (3 m) images demonstrate that our approach provides precise domain adaptation, preserving image content while improving radiometric accuracy and feature representation. A thorough image quality assessment and comparisons with the standard DDIM framework and five other leading methods are presented. We reach a mean learned perceptual image patch similarity of 0.1884 and a Fréchet Inception Distance of 45.64, expressively outperforming all compared methods, including DDIM, ShuffleMixer, and SwinIR. The usefulness of our approach is further demonstrated in two Heterogeneous Change Detection tasks.

Keywords

• Change detection algorithms
• deep learning
• diffusion models
• image denoising
• image enhancement
• image quality