# Deep Manifold Embedding for Hyperspectral Image Classification

arXiv: https://arxiv.org/abs/1912.11264 · HTML: https://mlsanity.com/paper/1912.11264/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems](https://mlsanity.com/paper/2108.08298/) (2021)
- [ManifoldNeRF: View-dependent Image Feature Supervision for Few-shot Neural Radiance Fields](https://mlsanity.com/paper/2310.13670/) (2023)
- [Cross-domain Hyperspectral Image Classification based on Bi-directional Domain Adaptation](https://mlsanity.com/paper/2507.02268/) (2025)
- [Histopathology Image Classification using Deep Manifold Contrastive Learning](https://mlsanity.com/paper/2306.14459/) (2023)
- [HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding](https://mlsanity.com/paper/2311.14899/) (2023)
- [Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction](https://mlsanity.com/paper/2106.11929/) (2021)
- [A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification](https://mlsanity.com/paper/2205.12459/) (2022)
- [Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification](https://mlsanity.com/paper/2310.18549/) (2023)
- [High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification](https://mlsanity.com/paper/2604.26279/) (2026)
- [MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification](https://mlsanity.com/paper/2310.18550/) (2023)

## Abstract (arXiv)

Deep learning methods have played a more and more important role in hyperspectral image classification. However, the general deep learning methods mainly take advantage of the information of sample itself or the pairwise information between samples while ignore the intrinsic data structure within the whole data. To tackle this problem, this work develops a novel deep manifold embedding method(DMEM) for hyperspectral image classification. First, each class in the image is modelled as a specific nonlinear manifold and the geodesic distance is used to measure the correlation between the samples. Then, based on the hierarchical clustering, the manifold structure of the data can be captured and each nonlinear data manifold can be divided into several sub-classes. Finally, considering the distribution of each sub-class and the correlation between different subclasses, the DMEM is constructed to preserve the estimated geodesic distances on the data manifold between the learned low dimensional features of different samples. Experiments over three real-world hyperspectral image datasets have demonstrated the effectiveness of the proposed method.