Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives
<|reference_start|>Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives: 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.<|reference_end|>
@article{liu2024diffusion,
    title={Diffusion Models Meet Remote Sensing: Principles, Methods, and
  Perspectives},
    author={Yidan Liu, Jun Yue, Shaobo Xia, Pedram Ghamisi, Weiying Xie, Leyuan
  Fang},
    journal={in IEEE Transactions on Geoscience and Remote Sensing, vol. 62,
  pp. 1-22, 2024, Art no. 4708322},
    year={2024},
    doi={10.1109/TGRS.2024.3464685},
    archivePrefix={arXiv},
    eprint={2404.08926},
    primaryClass={cs.CV}
}