Hyperspectral Image (HSI) classification is an important issue in remote sensing field with extensive applications in earth science. In recent years, a large number of deep learning-based HSI classification methods have been proposed. However, existing methods have limited ability to handle high-dimensional, highly redundant, and complex data, making it challenging to capture the spectral-spatial distributions of data and relationships between samples. To address this issue, we propose a generative framework for HSI classification with diffusion models (SpectralDiff) that effectively mines the distribution information of high-dimensional and highly redundant data by iteratively denoising and explicitly constructing the data generation process, thus better reflecting the relationships between samples. The framework consists of a spectral-spatial diffusion module, and an attention-based classification module. The spectral-spatial diffusion module adopts forward and reverse spectral-spatial diffusion processes to achieve adaptive construction of sample relationships without requiring prior knowledge of graphical structure or neighborhood information. It captures spectral-spatial distribution and contextual information of objects in HSI and mines unsupervised spectral-spatial diffusion features within the reverse diffusion process. Finally, these features are fed into the attention-based classification module for per-pixel classification. The diffusion features can facilitate cross-sample perception via reconstruction distribution, leading to improved classification performance. Experiments on three public HSI datasets demonstrate that the proposed method can achieve better performance than state-of-the-art methods. For the sake of reproducibility, the source code of SpectralDiff will be publicly available at https://github.com/chenning0115/SpectralDiff.

翻译：高光谱图像（HSI）分类是遥感领域的重要课题，在地球科学中具有广泛应用。近年来，大量基于深度学习的高光谱图像分类方法被提出。然而，现有方法在处理高维、高冗余和复杂数据方面能力有限，难以捕捉数据的光谱-空间分布及样本间关系。为解决此问题，我们提出了一种基于扩散模型的高光谱图像分类生成式框架（SpectralDiff），该框架通过迭代去噪和显式构建数据生成过程，有效挖掘高维高冗余数据的分布信息，从而更好地反映样本间关系。该框架包含光谱-空间扩散模块和基于注意力机制的分类模块。光谱-空间扩散模块采用正向和反向光谱-空间扩散过程，在无需图结构或邻域信息先验知识的情况下实现样本关系的自适应构建。该模块能够捕获高光谱图像中物体的光谱-空间分布与上下文信息，并在反向扩散过程中挖掘无监督的光谱-空间扩散特征。最后，这些特征被输入基于注意力机制的分类模块进行逐像素分类。扩散特征可通过重建分布促进跨样本感知，从而提升分类性能。在三个公开高光谱数据集上的实验表明，所提方法能够取得优于现有最优方法的性能。为保证可复现性，SpectralDiff的源代码将在https://github.com/chenning0115/SpectralDiff 公开。