# Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model

arXiv: https://arxiv.org/abs/1904.13017 · HTML: https://mlsanity.com/paper/1904.13017/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

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

- [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)
- [AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising](https://mlsanity.com/paper/2307.00269/) (2023)
- [Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing](https://mlsanity.com/paper/2312.13127/) (2023)
- [Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders](https://mlsanity.com/paper/2403.04526/) (2024)
- [Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing](https://mlsanity.com/paper/2310.02340/) (2023)
- [Hybrid Deep Learning for Hyperspectral Single Image Super-Resolution](https://mlsanity.com/paper/2510.00033/) (2025)
- [GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness](https://mlsanity.com/paper/2204.07713/) (2022)
- [DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing](https://mlsanity.com/paper/2503.03465/) (2025)
- [Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders](https://mlsanity.com/paper/2303.08156/) (2023)
- [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)
- [SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2510.22607/) (2025)
- [Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximization](https://mlsanity.com/paper/2512.05674/) (2025)
- [Optical Inversion and Spectral Unmixing of Spectroscopic Photoacoustic Images with Physics-Informed Neural Networks](https://mlsanity.com/paper/2602.16357/) (2026)
- [Looking into a Pixel by Nonlinear Unmixing -- A Generative Approach](https://mlsanity.com/paper/2604.01141/) (2026)

## Abstract (arXiv)

Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed individual pixels. Recently, nonlinear spectral unmixing has received particular attention because a linear mixture is not appropriate under many conditions. However, existing nonlinear unmixing approaches are often based on specific assumptions regarding the inherent nonlinearity, and they can be ineffective when applied to conditions deviating from the original assumptions. Therefore, these approaches are not well suited to scenes with unknown nonlinearity characteristics. This paper presents an unsupervised nonlinear spectral unmixing method based on a deep autoencoder network that applies to a generalized linear-mixture/nonlinear fluctuation model, consisting of a linear mixture component and an additive nonlinear mixture component that depends on both endmembers and abundances. The proposed approach benefits from the universal modeling ability of deep neural networks to learn the inherent nonlinearity of the nonlinear mixture component from the data itself via the autoencoder network, rather than relying on an assumed form. Extensive experiments with numerically synthetic, labeled laboratory-created data and real airborne data, illustrate the generality and effectiveness of this approach compared with state-of-the-art methods.