# Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing

arXiv: https://arxiv.org/abs/1902.05528 · HTML: https://mlsanity.com/paper/1902.05528/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2019.

## Built on by (most cited first, 16 of 16)

- [Deep Hyperspectral Unmixing using Transformer Network](https://mlsanity.com/paper/2203.17076/) (2022)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks](https://mlsanity.com/paper/2303.10566/) (2023)
- [Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing](https://mlsanity.com/paper/2104.08409/) (2021)
- [Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder](https://mlsanity.com/paper/2203.01327/) (2022)
- [AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising](https://mlsanity.com/paper/2307.00269/) (2023)
- [Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing](https://mlsanity.com/paper/2310.02340/) (2023)
- [Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation](https://mlsanity.com/paper/2109.13748/) (2021)
- [SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing](https://mlsanity.com/paper/2311.10701/) (2023)
- [Weighted-Sum of Gaussian Process Latent Variable Models](https://mlsanity.com/paper/2402.09122/) (2024)
- [Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning](https://mlsanity.com/paper/2402.03398/) (2024)
- [Neural Network for Blind Unmixing: a novel MatrixConv Unmixing (MCU) Approach](https://mlsanity.com/paper/2503.08745/) (2025)
- [ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery](https://mlsanity.com/paper/2505.18546/) (2025)
- [Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance](https://mlsanity.com/paper/2508.08431/) (2025)
- [SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2510.22607/) (2025)
- [Latent Dirichlet Transformer VAE for Hyperspectral Unmixing with Bundled Endmembers](https://mlsanity.com/paper/2511.17757/) (2025)

## Abstract (arXiv)

Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models still lack the compromise between flexibility and low-dimensional representation that is necessary to properly explore the fact that spectral variability is often confined to a low-dimensional manifold in real scenes. In this paper we propose to learn a spectral variability model directly form the observed data, instead of imposing it a priori. This is achieved through a deep generative EM model, which is estimated using a variational autoencoder (VAE). The encoder and decoder that compose the generative model are trained using pure pixel information extracted directly from the observed image, what allows for an unsupervised formulation. The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model. Simulations using both synthetic and real data indicate that the proposed strategy can outperform the competing state-of-the-art algorithms.