# Nonlinear unmixing of hyperspectral images: models and algorithms

arXiv: https://arxiv.org/abs/1304.1875 · HTML: https://mlsanity.com/paper/1304.1875/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2013.

## Built on by (most cited first, 27 of 27)

- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder](https://mlsanity.com/paper/2106.09070/) (2021)
- [Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixing](https://mlsanity.com/paper/2103.16204/) (2021)
- [Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing](https://mlsanity.com/paper/2104.08409/) (2021)
- [Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing](https://mlsanity.com/paper/2205.03798/) (2022)
- [Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification](https://mlsanity.com/paper/2412.03893/) (2024)
- [Unrolling PALM for sparse semi-blind source separation](https://mlsanity.com/paper/2112.05694/) (2021)
- [AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising](https://mlsanity.com/paper/2307.00269/) (2023)
- [Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders](https://mlsanity.com/paper/2403.04526/) (2024)
- [Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing](https://mlsanity.com/paper/2503.19258/) (2025)
- [Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing](https://mlsanity.com/paper/2310.02340/) (2023)
- [Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programming](https://mlsanity.com/paper/2404.13098/) (2024)
- [GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness](https://mlsanity.com/paper/2204.07713/) (2022)
- [SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing](https://mlsanity.com/paper/2311.10701/) (2023)
- [DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing](https://mlsanity.com/paper/2503.03465/) (2025)
- [SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing](https://mlsanity.com/paper/2102.05713/) (2021)
- [Approximation of radiative transfer for surface spectral features](https://mlsanity.com/paper/2302.02641/) (2023)
- [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)
- [Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework](https://mlsanity.com/paper/2407.14899/) (2024)
- [Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis](https://mlsanity.com/paper/2407.15321/) (2024)
- [Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the art](https://mlsanity.com/paper/2507.14260/) (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)
- [A Variational Latent-Space Framework for Uncertainty-Aware Spectral Image Emulation](https://mlsanity.com/paper/2603.21911/) (2026)
- [Looking into a Pixel by Nonlinear Unmixing -- A Generative Approach](https://mlsanity.com/paper/2604.01141/) (2026)
- [A Deep Equilibrium Network for Hyperspectral Unmixing](https://mlsanity.com/paper/2604.11279/) (2026)

## Abstract (arXiv)

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid and other nonlinear models need to be considered, for instance, when there are multi-scattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this paper, we present an overview of recent advances in nonlinear unmixing modeling.