.png){kind=icon}
Return
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3464685.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Diffusion models
Training
Remote sensing
Diffusion processes
Noise reduction
Reviews
Gaussian distribution
Deep learning
diffusion models
generative models
remote sensing (RS)
Journal
IF:
8.6
Papers:
2.2W
Citations:
10.7W
Organization
View more organizations
Cited Papers
ShareSaveShareSavePerformance enhancement of MgZnO ultraviolet photodetectors using ultrathin Al2O3 inserted layer
PREAI
ShareSaveSTFDiff: Remote sensing image spatiotemporal fusion with diffusion models.png)
INFORMATION FUSION
INFORMATION FUSIONIF15.5
PREAI
ShareSave
ShareSaveShareSaveShareSave View more