Optical Image-to-Image Translation Using Denoising Diffusion Models:
  Heterogeneous Change Detection as a Use Case
João Gabriel Vinholi Marco Chini Anis Amziane Renato Machado Danilo Silva Patrick Matgen
We introduce an innovative deep learning-based method that uses a denoising
diffusion-based model to translate low-resolution images to high-resolution
ones from different optical sensors while preserving the contents and avoiding
undesired artifacts. The proposed method is trained and tested on a large and
diverse data set of paired Sentinel-II and Planet Dove images. We show that it
can solve serious image generation issues observed when the popular
classifier-free guided Denoising Diffusion Implicit Model (DDIM) framework is
used in the task of Image-to-Image Translation of multi-sensor optical remote
sensing images and that it can generate large images with highly consistent
patches, both in colors and in features. Moreover, we demonstrate how our
method improves heterogeneous change detection results in two urban areas:
Beirut, Lebanon, and Austin, USA. Our contributions are: i) a new training and
testing algorithm based on denoising diffusion models for optical image
translation; ii) a comprehensive image quality evaluation and ablation study;
iii) a comparison with the classifier-free guided DDIM framework; and iv)
change detection experiments on heterogeneous data.