Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives

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 (RS) 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 RS, it is necessary to conduct a comprehensive review of existing diffusion model-based RS papers, to help researchers recognize the potential of diffusion models and provide some directions for further exploration. Specifically, this article first introduces the theoretical background of diffusion models, and then systematically reviews the applications of diffusion models in RS, including image generation, enhancement, and interpretation. Finally, the limitations of existing RS diffusion models and worthy research directions for further exploration are discussed and summarized.

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Cited by in corpus (3)

• Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives
• Mask Approximation Net: A Novel Diffusion Model Approach for Remote Sensing Change Captioning
• Survey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Models