Hyperspectral image (HSI) classification is an important topic in the field of remote sensing, and has a wide range of applications in Earth science. HSIs contain hundreds of continuous bands, which are characterized by high dimension and high correlation between adjacent bands. The high dimension and redundancy of HSI data bring great difficulties to HSI classification. In recent years, a large number of HSI feature extraction and classification methods based on deep learning have been proposed. However, their ability to model the global relationships among samples in both spatial and spectral domains is still limited. In order to solve this problem, an HSI classification method with spectral-spatial diffusion models is proposed. The proposed method realizes the reconstruction of spectral-spatial distribution of the training samples with the forward and reverse spectral-spatial diffusion process, thus modeling the global spatial-spectral relationship between samples. Then, we use the spectral-spatial denoising network of the reverse process to extract the unsupervised diffusion features. Features extracted by the spectral-spatial diffusion models can achieve cross-sample perception from the reconstructed distribution of the training samples, thus obtaining better classification performance. Experiments on three public HSI datasets show that the proposed method can achieve better performance than the state-of-the-art methods. The source code and the pre-trained spectral-spatial diffusion model will be publicly available at https://github.com/chenning0115/SpectralDiff.

翻译：摘要：高光谱图像分类是遥感领域的重要课题，在地球科学中具有广泛应用。高光谱图像包含数百个连续波段，具有高维度和相邻波段高度相关的特点。这种高维度与冗余性给高光谱图像分类带来了巨大困难。近年来，基于深度学习的高光谱图像特征提取与分类方法被大量提出，但其在空间域与光谱域中建模样本间全局关系的能力仍显不足。为解决这一问题，本文提出了一种基于光谱-空间扩散模型的高光谱图像分类方法。该方法通过正向和反向的光谱-空间扩散过程实现对训练样本光谱-空间分布的重建，从而建模样本间的全局空间-光谱关系；进而利用反向过程中的光谱-空间去噪网络提取无监督扩散特征。由光谱-空间扩散模型提取的特征能够从训练样本的重建分布中实现跨样本感知，从而获得更优的分类性能。在三个公开高光谱数据集上的实验表明，所提方法能取得优于现有最先进方法的性能。相关源代码与预训练的光谱-空间扩散模型将在https://github.com/chenning0115/SpectralDiff 公开发布。