# High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/2604.26279 · HTML: https://mlsanity.com/paper/2604.26279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 13 of 13)

- [SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models](https://mlsanity.com/paper/2304.05961/) (2023)
- [FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal](https://mlsanity.com/paper/2109.08879/) (2021)
- [A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models](https://mlsanity.com/paper/2404.14955/) (2024)
- [Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning](https://mlsanity.com/paper/1812.09530/) (2018)
- [Deep Manifold Embedding for Hyperspectral Image Classification](https://mlsanity.com/paper/1912.11264/) (2019)
- [Hybrid Convolutional and Attention Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2403.10067/) (2024)
- [Dual Selective Fusion Transformer Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.03171/) (2024)
- [SLCGC: A lightweight Self-supervised Low-pass Contrastive Graph Clustering Network for Hyperspectral Images](https://mlsanity.com/paper/2502.03497/) (2025)
- [Graph-Weighted Contrastive Learning for Semi-Supervised Hyperspectral Image Classification](https://mlsanity.com/paper/2503.15731/) (2025)
- [Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2505.11158/) (2025)
- [Spectral-Spatial Self-Supervised Learning for Few-Shot Hyperspectral Image Classification](https://mlsanity.com/paper/2505.12482/) (2025)
- [Iterative Low-rank Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2509.00356/) (2025)
- [HyperTTA: Test-Time Adaptation for Hyperspectral Image Classification under Distribution Shifts](https://mlsanity.com/paper/2509.08436/) (2025)

## Built on by (most cited first, 1 of 1)

- [MSP-Net: Manifold-Guided Spectral Prompt Network for Hyperspectral Object Tracking](https://mlsanity.com/paper/2608.09575/) (2026)

## Abstract (arXiv)

Recently, Hyperspectral Image (HSI) classification has attracted increasing attention in remote sensing. However, HSI data are inherently high-dimensional but low-rank, with discriminative information concentrated on a low-dimensional latent manifold. In real-world remote sensing scenarios, the superposition of multiple degradation factors disrupts this intrinsic manifold structure, driving samples away from their original low-dimensional distribution and introducing substantial redundant and non-discriminative variations. To better handle this challenge, this paper proposes a manifold-space diffusion framework (MSDiff) for robust hyperspectral classification under complex degradation conditions. Specifically, the proposed method first maps high-dimensional, degradation-affected HSI data into a compact low-dimensional manifold through a discriminative spectral-spatial reconstruction task, preserving class semantics and reducing redundant variations. A diffusion-based generative model is then applied to regularize the spectral-spatial distribution within the manifold, enabling progressive refinement and stabilization of latent features against residual degradations. The key advantage of the proposed framework lies in performing diffusion-based distribution modeling directly on the low-dimensional manifold, effectively decoupling degradation-induced disturbances from intrinsic discriminative structures and enhancing representation stability under complex degradations. Experimental results on multiple hyperspectral benchmarks demonstrate consistent performance improvements over state-of-the-art methods under diverse composite degradation settings. The code will be available at https://github.com/yangboxiang1207/MSDiff