Learning effective spectral-spatial features is important for the hyperspectral image (HSI) classification task, but the majority of existing HSI classification methods still suffer from modeling complex spectral-spatial relations and characterizing low-level details and high-level semantics comprehensively. As a new class of record-breaking generative models, diffusion models are capable of modeling complex relations for understanding inputs well as learning both high-level and low-level visual features. Meanwhile, diffusion models can capture more abundant features by taking advantage of the extra and unique dimension of timestep t. In view of these, we propose an unsupervised spectral-spatial feature learning framework based on the diffusion model for HSI classification for the first time, named Diff-HSI. Specifically, we first pretrain the diffusion model with unlabeled HSI patches for unsupervised feature learning, and then exploit intermediate hierarchical features from different timesteps for classification. For better using the abundant timestep-wise features, we design a timestep-wise feature bank and a dynamic feature fusion module to construct timestep-wise features, adaptively learning informative multi-timestep representations. Finally, an ensemble of linear classifiers is applied to perform HSI classification. Extensive experiments are conducted on three public HSI datasets, and our results demonstrate that Diff-HSI outperforms state-of-the-art supervised and unsupervised methods for HSI classification.

翻译：学习有效的空谱特征对于高光谱图像分类任务至关重要，然而现有的大多数高光谱图像分类方法仍难以建模复杂的空谱关系，并全面刻画低层细节与高层语义。作为一种新型的创纪录生成模型，扩散模型能够建模复杂关系以深入理解输入，同时学习高层和低层视觉特征。此外，扩散模型可利用额外且独特的时刻维度捕捉更丰富的特征。鉴于此，我们首次提出了一种基于扩散模型的无监督空谱特征学习框架用于高光谱图像分类，命名为Diff-HSI。具体而言，我们首先使用无标注的高光谱图像块对扩散模型进行预训练以实现无监督特征学习，然后从不同时刻提取中间层次特征进行分类。为更好地利用丰富的逐时刻特征，我们设计了逐时刻特征库和动态特征融合模块来构建逐时刻特征，自适应地学习信息丰富的多时刻表征。最后，采用线性分类器集成进行高光谱图像分类。我们在三个公开高光谱数据集上进行了大量实验，结果表明Diff-HSI在高光谱图像分类中优于现有最先进的监督和无监督方法。