  As a newly emerging advance in deep generative models, diffusion models have
achieved state-of-the-art results in many fields, including computer vision,
natural language processing, and molecule design. The remote sensing community
has also noticed the powerful ability of diffusion models and quickly applied
them to a variety of tasks for image processing. Given the rapid increase in
research on diffusion models in the field of remote sensing, it is necessary to
conduct a comprehensive review of existing diffusion model-based remote sensing
papers, to help researchers recognize the potential of diffusion models and
provide some directions for further exploration. Specifically, this paper first
introduces the theoretical background of diffusion models, and then
systematically reviews the applications of diffusion models in remote sensing,
including image generation, enhancement, and interpretation. Finally, the
limitations of existing remote sensing diffusion models and worthy research
directions for further exploration are discussed and summarized.
