# Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing

arXiv: https://arxiv.org/abs/2104.08409 · HTML: https://mlsanity.com/paper/2104.08409/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing](https://mlsanity.com/paper/1708.01894/) (2017)
- [Nonlinear unmixing of hyperspectral images: models and algorithms](https://mlsanity.com/paper/1304.1875/) (2013)
- [Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing](https://mlsanity.com/paper/1902.05528/) (2019)
- [Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing](https://mlsanity.com/paper/1712.01770/) (2017)
- [A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability](https://mlsanity.com/paper/1808.01047/) (2018)
- [Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis](https://mlsanity.com/paper/1909.09741/) (2019)
- [Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images](https://mlsanity.com/paper/1503.05521/) (2015)

## Built on by (most cited first, 6 of 6)

- [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)
- [Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering](https://mlsanity.com/paper/2204.06471/) (2022)
- [Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing](https://mlsanity.com/paper/2310.02340/) (2023)
- [GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness](https://mlsanity.com/paper/2204.07713/) (2022)
- [ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery](https://mlsanity.com/paper/2505.18546/) (2025)

## Abstract (arXiv)

Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse. AECs are especially appealing for nonlinear HU since they lead to unsupervised and model-free algorithms. However, existing approaches fail to explore the fact that the encoder should invert the mixing process, which might reduce their robustness. In this paper, we propose a model-based AEC for nonlinear HU by considering the mixing model a nonlinear fluctuation over a linear mixture. Differently from previous works, we show that this restriction naturally imposes a particular structure to both the encoder and to the decoder networks. This introduces prior information in the AEC without reducing the flexibility of the mixing model. Simulations with synthetic and real data indicate that the proposed strategy improves nonlinear HU.