Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review

TL;DR

This review addresses hyperspectral image processing challenges posed by high dimensionality, redundancy, and noise, and surveys diffusion-model approaches as robust, high-fidelity generative tools. It categorizes diffusion models into DDPM, score-based models, and SDE-based formulations, and discusses their evolution, variants, and diffusion-based architectures in both conditional and latent forms. The survey then maps diffusion-model applications to hyperspectral tasks such as data enhancement, generation, classification, segmentation, anomaly and target detection, noise suppression, and data recovery, highlighting notable methods (e.g., CDM, LDM, SpectralDiff, Diff-Mosaic, BSDM, DWSDiff) and their performance–efficiency trade-offs. It also analyzes challenges including computational cost, data requirements, adaptability, and interpretability, and provides an outlook on integrating diffusion models with physics-based priors, CNN/Transformer hybrids, and automated design for real-world deployment. Overall, diffusion models promise improved accuracy and robustness for hyperspectral analysis, while signaling a need for lightweight, interpretable, and cross-domain research to enable operational adoption.

Abstract

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 models, as a class of emerging generative approaches, have demonstrated promising capabilities in hyperspectral image (HSI) processing tasks. By simulating the diffusion process of data in time, the Diffusion Model are capable of modeling high-dimensional spectral structures, generate high-quality samples, and achieve competitive performance in spectral-spatial denoising tasks 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.

Figures (19)

• Figure 1: Hyperspectral image (a) and Illustration of hyperspectral image (b)
• Figure 2: Diffusion processes in diffusion models ref17
• Figure 3: Evolution timeline of the Diffusion model
• Figure 4: Improved division of the diffusion model according to different variants
• Figure 5: Deep learning model-based hyperspectral image processing