Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications

• URL: http://arxiv.org/abs/2404.11243v4
• Date: Wed, 04 Dec 2024 11:23:37 GMT
• Title: Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications
• Authors: João Gabriel Vinholi, Marco Chini, Anis Amziane, Renato Machado, Danilo Silva, Patrick Matgen,
• Abstract summary: We propose a method that super-resolves large-scale low spatial resolution images into high-resolution equivalents from disparate optical sensors.<n>Our approach provides precise domain adaptation, preserving image content while improving radiometric accuracy and feature representation.<n>We reach a mean Learned Perceptual Image Patch Similarity (mLPIPS) of 0.1884 and a Fr'echet Inception Distance (FID) of 45.64, expressively outperforming all compared methods.
• Score: 3.4085512042262374
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 multi-patch 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 multi-sensor 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 (10m) and Planet Dove (3m) 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 (mLPIPS) of 0.1884 and a Fr\'echet Inception Distance (FID) 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.

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