Contrastdm: Combining Contrastive Learning and Diffusion Model for Hyperspectral Image Classification

In recent years, self-supervised learning has made rapid progress in the field of hyperspectral image classification(HSIC). However, different methods have their own advantages and disadvantages. Contrastive learning can extract key information from a large amount of redundant data, but its training method can lead to significant intra-class variance. Diffusion models can enhance features and aggregate similar ones, but they have a high computational cost due to the sampling process. We combine contrastive learning and diffusion models by training the diffusion model with low-dimensional features extracted from contrastive learning. This generates new artificial features to improve feature representation. We name this new method ContrastDM. The results of classification experiments on two publicly available datasets demonstrate that ContrastDM significantly outperforms the compared state-of-the-art (SOTA) methods.