Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912142660206592 |
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| author | Vinholi, João Gabriel Chini, Marco Amziane, Anis Machado, Renato Silva, Danilo Matgen, Patrick |
| author_facet | Vinholi, João Gabriel Chini, Marco Amziane, Anis Machado, Renato Silva, Danilo Matgen, Patrick |
| contents | 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 Fréchet 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11243 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications Vinholi, João Gabriel Chini, Marco Amziane, Anis Machado, Renato Silva, Danilo Matgen, Patrick Computer Vision and Pattern Recognition Artificial Intelligence 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 Fréchet 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. |
| title | Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.11243 |