When Hyperspectral Image Classification Meets Diffusion Models: An
  Unsupervised Feature Learning Framework
Jingyi Zhou Jiamu Sheng Jiayuan Fan Peng Ye Tong He Bin Wang Tao Chen
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.