  The effectiveness of spectral-spatial feature learning is crucial for the
hyperspectral image (HSI) classification task. Diffusion models, as a new class
of groundbreaking generative models, have the ability to learn both contextual
semantics and textual details from the distinct timestep dimension, enabling
the modeling of complex spectral-spatial relations in HSIs. However, existing
diffusion-based HSI classification methods only utilize manually selected
single-timestep single-stage features, limiting the full exploration and
exploitation of rich contextual semantics and textual information hidden in the
diffusion model. To address this issue, we propose a novel diffusion-based
feature learning framework that explores Multi-Timestep Multi-Stage Diffusion
features for HSI classification for the first time, called MTMSD. Specifically,
the diffusion model is first pretrained with unlabeled HSI patches to mine the
connotation of unlabeled data, and then is used to extract the multi-timestep
multi-stage diffusion features. To effectively and efficiently leverage
multi-timestep multi-stage features,two strategies are further developed. One
strategy is class & timestep-oriented multi-stage feature purification module
with the inter-class and inter-timestep prior for reducing the redundancy of
multi-stage features and alleviating memory constraints. The other one is
selective timestep feature fusion module with the guidance of global features
to adaptively select different timestep features for integrating texture and
semantics. Both strategies facilitate the generality and adaptability of the
MTMSD framework for diverse patterns of different HSI data. Extensive
experiments are conducted on four public HSI datasets, and the results
demonstrate that our method outperforms state-of-the-art methods for HSI
classification, especially on the challenging Houston 2018 dataset.
