SWDiff: Stage-Wise Hyperspectral Diffusion Model for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) has been a popular task in recent years. Even benefiting from the rapid development of deep neural networks (DNNs), there are still remaining intrinsic problems, including inadequate utilization of spatial-spectral information and insufficient labeled samples. The recent emergency of diffusion models (DMs) came to the fore because of their impressive refined image generation performance. DMs have been proven to not only can capture the underlying information of data through training the decoder of DMs, but also have more stable training than GANs while retaining even better performance. To better perceive and utilize spectral-spatial information while alleviating insufficient labeled samples simultaneously, we introduce the DM into HSIC from a data generation perspective. Specifically, we propose a stage-wise DM framework (SWDiff), dividing the HSIC task into three stages, including: pretrain the diffusion decoder with the hyperspectral image (HSI); generate new HSI cubes through the well-trained decoder to extra supply the original HSI set; and utilize the supplied dataset to train varied classifiers to obtain a better classification performance. Suitable pretraining could enable the decoder to acquire spatial-spectral information of the HSIs sufficiently via modeling spectral-spatial relationships across samples, leading to better utilization of spectral and spatial information of HSIs. Furthermore, the DM could provide the inference stage with spatial-spectral prior knowledge to ensure the feasibility and plausibility of the dataset complement, which could alleviate the insufficient labeled samples problem. Eventually, the classification stage will benefit from the first two stages.