  In the field of remote sensing, the challenge of comparing images captured by
disparate sensors is a common obstacle. 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 is tailored to super-resolve large-scale low spatial resolution images
into high-resolution equivalents from disparate optical sensors, ensuring
uniformity across hundreds of patches. Extensive experiments with a dataset of
paired Sentinel-II and Planet Dove images show that our approach provides
precise domain adaptation and artifact reduction. Our technique preserves the
image content while also improving radiometric (color) accuracy and feature
representations. 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 efficacy of our approach is further demonstrated by substantial
enhancements in HCD tasks performed in the urban settings of Beirut, Lebanon,
and Austin, USA.
