  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.
