# Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning

arXiv: https://arxiv.org/abs/1812.09530 · HTML: https://mlsanity.com/paper/1812.09530/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2018.

## Built on by (most cited first, 11 of 11)

- [SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models](https://mlsanity.com/paper/2304.05961/) (2023)
- [Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2404.18401/) (2024)
- [3DSS-Mamba: 3D-Spectral-Spatial Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2405.12487/) (2024)
- [IGroupSS-Mamba: Interval Group Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2410.05100/) (2024)
- [Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image](https://mlsanity.com/paper/2104.11904/) (2021)
- [Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging](https://mlsanity.com/paper/2505.00805/) (2025)
- [Graph Embedding via High Dimensional Model Representation for Hyperspectral Images](https://mlsanity.com/paper/2111.14680/) (2021)
- [High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification](https://mlsanity.com/paper/2604.26279/) (2026)
- [A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks](https://mlsanity.com/paper/2103.06383/) (2021)
- [Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning](https://mlsanity.com/paper/2103.13710/) (2021)
- [A Kernel-Based Modular Discriminant Analysis Framework for Small-Sample Learning](https://mlsanity.com/paper/2609.09910/) (2026)

## Abstract (arXiv)

The graph embedding (GE) methods have been widely applied for dimensionality reduction of hyperspectral imagery (HSI). However, a major challenge of GE is how to choose proper neighbors for graph construction and explore the spatial information of HSI data. In this paper, we proposed an unsupervised dimensionality reduction algorithm termed spatial-spectral manifold reconstruction preserving embedding (SSMRPE) for HSI classification. At first, a weighted mean filter (WMF) is employed to preprocess the image, which aims to reduce the influence of background noise. According to the spatial consistency property of HSI, the SSMRPE method utilizes a new spatial-spectral combined distance (SSCD) to fuse the spatial structure and spectral information for selecting effective spatial-spectral neighbors of HSI pixels. Then, it explores the spatial relationship between each point and its neighbors to adjusts the reconstruction weights for improving the efficiency of manifold reconstruction. As a result, the proposed method can extract the discriminant features and subsequently improve the classification performance of HSI. The experimental results on PaviaU and Salinas hyperspectral datasets indicate that SSMRPE can achieve better classification accuracies in comparison with some state-of-the-art methods.