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.

翻译：光谱-空间特征学习的有效性对于高光谱图像（HSI）分类任务至关重要。扩散模型作为一类突破性的新型生成模型，能够从独特的时间步维度学习上下文语义与纹理细节，从而实现对HSI中复杂光谱-空间关系的建模。然而，现有基于扩散的HSI分类方法仅利用人工选择的单时间步单阶段特征，限制了对扩散模型中隐藏的丰富上下文语义与纹理信息的充分挖掘与利用。为解决此问题，我们首次提出一种探索多时间步多阶段扩散特征用于HSI分类的新型扩散特征学习框架，称为MTMSD。具体而言，首先使用无标签HSI图像块预训练扩散模型以挖掘无标签数据的内涵，随后利用该模型提取多时间步多阶段扩散特征。为高效且有效地利用多时间步多阶段特征，我们进一步提出两种策略：其一是面向类别与时间步的多阶段特征纯化模块，通过引入类间与时间步间先验来减少多阶段特征的冗余并缓解内存限制；其二是选择性时间步特征融合模块，在全局特征引导下自适应选择不同时间步特征以整合纹理与语义信息。两种策略共同提升了MTMSD框架对于不同HSI数据多样模式的泛化性与适应性。在四个公开HSI数据集上的大量实验表明，我们的方法在HSI分类任务上优于现有先进方法，尤其在具有挑战性的Houston 2018数据集上表现突出。