Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications
<|reference_start|>Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications: 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 classic computer vision. However, these models struggle with large-scale multi-patch imagery, often focusing solely on small patches and resulting in inconsistencies across the full image. To overcome these limitations, we propose a novel method that leverages DDIM for effective 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, which are crucial for addressing the challenges of large-scale image translation. Extensive experiments with a dataset of paired Sentinel-II and Planet Dove images demonstrate that our approach provides precise domain adaptation and artifact reduction. The proposed method preserves image content while improving radiometric (color) accuracy and feature representation. The outcome is a high-resolution large-scale image with consistent patches, vital for applications such as heterogeneous change detection (HCD). We present a unique training and testing algorithm rooted in DDIMs, a thorough image quality assessment, and a comparative study against the standard classifier-free guided DDIM framework and five other leading methods. The usefulness of our approach is further demonstrated in HCD tasks performed in the urban settings of Beirut, Lebanon, and Austin, USA.<|reference_end|>
@article{vinholi2024multi-sensor,
    title={Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale
  Applications},
    author={Jo~ao Gabriel Vinholi, Marco Chini, Anis Amziane, Renato Machado,
  Danilo Silva, Patrick Matgen},
    journal={arXiv preprint arXiv:2404.11243},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11243},
    primaryClass={cs.CV cs.AI}
}