# Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

arXiv: https://arxiv.org/abs/2308.09375 · HTML: https://mlsanity.com/paper/2308.09375/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 240 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 30 of 38)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing](https://mlsanity.com/paper/1810.12000/) (2018)
- [Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review](https://mlsanity.com/paper/2001.07307/) (2020)
- [Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)](https://mlsanity.com/paper/2003.02822/) (2020)
- [Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtraction](https://mlsanity.com/paper/1512.01003/) (2015)
- [EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing](https://mlsanity.com/paper/1708.01894/) (2017)
- [Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing](https://mlsanity.com/paper/2105.10194/) (2021)
- [Nonlinear unmixing of hyperspectral images: models and algorithms](https://mlsanity.com/paper/1304.1875/) (2013)
- [Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case](https://mlsanity.com/paper/1406.5273/) (2014)
- [Image Restoration for Remote Sensing: Overview and Toolbox](https://mlsanity.com/paper/2107.00557/) (2021)
- [Deep Hyperspectral Unmixing using Transformer Network](https://mlsanity.com/paper/2203.17076/) (2022)
- [Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization](https://mlsanity.com/paper/1708.02883/) (2017)
- [Hyperspectral unmixing with spectral variability using a perturbed linear mixing model](https://mlsanity.com/paper/1502.01260/) (2015)
- [Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing](https://mlsanity.com/paper/1902.05528/) (2019)
- [Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model](https://mlsanity.com/paper/1904.13017/) (2019)
- [Fast and Robust Archetypal Analysis for Representation Learning](https://mlsanity.com/paper/1405.6472/) (2014)
- [Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms](https://mlsanity.com/paper/1805.10232/) (2018)
- [Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches](https://mlsanity.com/paper/1507.01661/) (2015)
- [A Plug-and-Play Priors Framework for Hyperspectral Unmixing](https://mlsanity.com/paper/2012.13074/) (2020)
- [Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)](https://mlsanity.com/paper/1506.04870/) (2015)
- [Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing](https://mlsanity.com/paper/1712.01770/) (2017)
- [Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing](https://mlsanity.com/paper/2209.11002/) (2022)
- [Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2206.05508/) (2022)
- [Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review](https://mlsanity.com/paper/2205.06407/) (2022)
- [Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing](https://mlsanity.com/paper/1504.01515/) (2015)
- [Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing](https://mlsanity.com/paper/2001.00425/) (2020)
- [Generalized linear mixing model accounting for endmember variability](https://mlsanity.com/paper/1710.07723/) (2017)
- [Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing](https://mlsanity.com/paper/2007.14033/) (2020)
- [Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder](https://mlsanity.com/paper/2106.09070/) (2021)
- [Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability](https://mlsanity.com/paper/1510.05893/) (2015)

## Built on by (most cited first, 12 of 12)

- [HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagery](https://mlsanity.com/paper/2503.21841/) (2025)
- [A Survey on Archetypal Analysis](https://mlsanity.com/paper/2504.12392/) (2025)
- [MLXP: A Framework for Conducting Replicable Experiments in Python](https://mlsanity.com/paper/2402.13831/) (2024)
- [Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing](https://mlsanity.com/paper/2503.19258/) (2025)
- [RamanPFN: learning from Raman spectral structure with a tabular foundation model](https://mlsanity.com/paper/2608.02157/) (2026)
- [Fast Semisupervised Unmixing Using Nonconvex Optimization](https://mlsanity.com/paper/2401.12609/) (2024)
- [Theoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspective](https://mlsanity.com/paper/2408.07580/) (2024)
- [Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis](https://mlsanity.com/paper/2506.13484/) (2025)
- [SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2510.22607/) (2025)
- [MiSiSUn: Minimum Simplex Semisupervised Unmixing](https://mlsanity.com/paper/2603.20263/) (2026)
- [Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning](https://mlsanity.com/paper/2603.29438/) (2026)
- [Hyperspectral Unmixing Hierarchies](https://mlsanity.com/paper/2604.16969/) (2026)

## Abstract (arXiv)

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image processing and machine learning substantially affected unmixing. This paper provides an overview of advanced and conventional unmixing approaches. Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories. We compare the performance of the unmixing techniques on three simulated and two real datasets. The experimental results reveal the advantages of different unmixing categories for different unmixing scenarios. Moreover, we provide an open-source Python-based package available at https://github.com/BehnoodRasti/HySUPP to reproduce the results.