# Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

arXiv: https://arxiv.org/abs/1406.5071 · HTML: https://mlsanity.com/paper/1406.5071/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2014.

## Built on by (most cited first, 7 of 7)

- [Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks](https://mlsanity.com/paper/2303.10566/) (2023)
- [Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders](https://mlsanity.com/paper/2106.12472/) (2021)
- [Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images](https://mlsanity.com/paper/2110.09744/) (2021)
- [Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing](https://mlsanity.com/paper/2310.02340/) (2023)
- [Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing](https://mlsanity.com/paper/2505.20902/) (2025)
- [Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework](https://mlsanity.com/paper/2407.14899/) (2024)
- [A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles](https://mlsanity.com/paper/2505.14634/) (2025)

## Abstract (arXiv)

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to take into account their variability in the image. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed algorithm exploits the whole image to provide spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated via simulations conducted on both synthetic and real data.