Abstract
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.
Abstract (translated)
作为深度生成模型新兴领域的一个，扩散模型已经在许多领域取得了最先进的成果，包括计算机视觉、自然语言处理和分子设计。遥感社区也注意到了扩散模型的强大能力，并迅速将其应用于图像处理等各种任务。在遥感领域扩散模型研究的快速增加下，有必要对基于扩散模型的遥感论文进行全面回顾，以帮助研究人员认识到扩散模型的潜力并为进一步探索提供一些方向。具体来说，本文首先介绍了扩散模型的理论背景，然后系统地综述了扩散模型在遥感中的应用，包括图像生成、增强和解释。最后，讨论了现有遥感扩散模型的局限性，并为进一步的研究提供了有价值的方向。
URL
https://arxiv.org/abs/2404.08926
