Diffusion Model in Hyperspectral Image Processing and Analysis: A Review

Hyperspectral image processing and analysis has important application value in remote sensing, agriculture and environmental monitoring, but its high dimensionality, data redundancy and noise interference etc. bring great challenges to the analysis. Traditional models have limitations in dealing with these complex data, and it is difficult to meet the increasing demand for analysis. In recent years, Diffusion Model, as an emerging generative model, has shown unique advantages in hyperspectral image processing. By simulating the diffusion process of data in time, the Diffusion Model can effectively process high-dimensional data, generate high-quality samples, and perform well in denoising and data enhancement. In this paper, we review the recent research advances in diffusion modeling for hyperspectral image processing and analysis, and discuss its applications in tasks such as high-dimensional data processing, noise removal, classification, and anomaly detection. The performance of diffusion-based models on image processing is compared and the challenges are summarized. It is shown that the diffusion model can significantly improve the accuracy and efficiency of hyperspectral image analysis, providing a new direction for future research.