Title: Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review

Abstract: Hyperspectral image (HSI) analysis is vital for applications in environmental monitoring, agriculture, and remote sensing. Nevertheless, conventional techniques frequently face challenges in managing the noise, spectral redundancy, and high dimensionality characteristic of HSI data, which restricts their precision and scalability. Recently, diffusion models—encompassing denoising diffusion probabilistic models and generative frameworks grounded in stochastic differential equations—have demonstrated significant promise. These approaches excel at capturing intricate spectral-spatial structures and producing high-fidelity HSI data, providing robust solutions for tasks including anomaly detection, classification, data augmentation, and noise suppression.

This paper offers a systematic overview of the latest developments in applying diffusion models to HSI processing. We organize existing methodologies, emphasize their efficacy in managing high-dimensional data, and benchmark their performance against traditional methods. Particular focus is placed on key applications like post-disaster anomaly identification and change detection. Furthermore, the review addresses existing constraints, such as training instability and high computational demands, while proposing avenues for future investigation. Our primary contributions include a comprehensive taxonomy of diffusion-based HSI methods, an examination of their utility across major remote sensing tasks, and insights into potential future research trajectories. By synthesizing these efforts, this review aims to assist the research community in leveraging deep learning to enable more efficient and effective hyperspectral image analysis.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC