# DiffSpectralNet : Unveiling the Potential of Diffusion Models for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2312.12441 · HTML: https://mlsanity.com/paper/2312.12441/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 29 Oct 2023.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 12 of 12)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Label-Efficient Semantic Segmentation with Diffusion Models](https://mlsanity.com/paper/2112.03126/) (2021)
- [SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers](https://mlsanity.com/paper/2107.02988/) (2021)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [Analyzing Bias in Diffusion-based Face Generation Models](https://mlsanity.com/paper/2305.06402/) (2023)
- [Diffusion Model for Camouflaged Object Detection](https://mlsanity.com/paper/2308.00303/) (2023)
- [Hyperspectral Image Classification with Attention Aided CNNs](https://mlsanity.com/paper/2005.11977/) (2020)
- [Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders](https://mlsanity.com/paper/1511.02916/) (2015)
- [Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks](https://mlsanity.com/paper/2012.10932/) (2020)

## Built on by (most cited first, 4 of 4)

- [Explore the Limits of Omni-modal Pretraining at Scale](https://mlsanity.com/paper/2406.09412/) (2024)
- [Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2505.11158/) (2025)
- [Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2505.07866/) (2025)
- [SDHSI-Net: Learning Better Representations for Hyperspectral Images via Self-Distillation](https://mlsanity.com/paper/2601.07416/) (2026)

## Abstract (arXiv)

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.