DiffSpectralNet : Unveiling the Potential of Diffusion Models for
  Hyperspectral Image Classification
Neetu Sigger Tuan Thanh Nguyen Gianluca Tozzi Quoc-Tuan Vien Sinh Van Nguyen
Hyperspectral images (HSI) have become popular for analysing remotely sensed
images in multiple domain like agriculture, medical. However, existing models
struggle with complex relationships and characteristics of spectral-spatial
data due to the multi-band nature and data redundancy of hyperspectral data. To
address this limitation, we propose a new network called DiffSpectralNet, which
combines diffusion and transformer techniques. Our approach involves a two-step
process. First, we use an unsupervised learning framework based on the
diffusion model to extract both high-level and low-level spectral-spatial
features. The diffusion method is capable of extracting diverse and meaningful
spectral-spatial features, leading to improvement in HSI classification. Then,
we employ a pretrained denoising U-Net to extract intermediate hierarchical
features for classification. Finally, we use a supervised transformer-based
classifier to perform the HSI classification. Through comprehensive experiments
on HSI datasets, we evaluate the classification performance of DiffSpectralNet.
The results demonstrate that our framework significantly outperforms existing
approaches, achieving state-of-the-art performance.