mlsanityData to 14 Oct 2026

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

arXiv 2308.09375 ↗First posted 18 Aug 2023eess.IV cs.CV cs.LGcited by 12

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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.

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

38 of 240 references.

References · 38

  1. 2017Deep Image Priorcited by 1,098
  2. 2018An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixingcited by 66
  3. 2020Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Reviewcited by 46
  4. 2020Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)cited by 36
  5. 2015Weighted Schatten p-Norm Minimization for Image Denoising and Background Subtractioncited by 29
  6. 2017EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixingcited by 29
  7. 2021Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixingcited by 29
  8. 2013Nonlinear unmixing of hyperspectral images: models and algorithmscited by 27
  9. 2014Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Casecited by 26
  10. 2021Image Restoration for Remote Sensing: Overview and Toolboxcited by 26
  11. 2022Deep Hyperspectral Unmixing using Transformer Networkcited by 22
  12. 2017Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimizationcited by 18
26 more
  1. 2015Hyperspectral unmixing with spectral variability using a perturbed linear mixing modelcited by 17
  2. 2019Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixingcited by 16
  3. 2019Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Modelcited by 16
  4. 2014Fast and Robust Archetypal Analysis for Representation Learningcited by 15
  5. 2018Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Normscited by 14
  6. 2015Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatchescited by 13
  7. 2020A Plug-and-Play Priors Framework for Hyperspectral Unmixingcited by 13
  8. 2015Robust Collaborative Nonnegative Matrix Factorization For Hyperspectral Unmixing (R-CoNMF)cited by 11
  9. 2017Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixingcited by 11
  10. 2022Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixingcited by 11
  11. 2022Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixingcited by 10
  12. 2022Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Reviewcited by 9
  13. 2015Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixingcited by 8
  14. 2020Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixingcited by 8
  15. 2017Generalized linear mixing model accounting for endmember variabilitycited by 7
  16. 2020Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixingcited by 7
  17. 2021Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencodercited by 7
  18. 2015Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variabilitycited by 6
  19. 2019Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysiscited by 6
  20. 2021Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixingcited by 6
  21. 2021Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixingcited by 6
  22. 2023SUnAA: Sparse Unmixing using Archetypal Analysiscited by 5
  23. 2016Hyperspectral Subspace Identification Using SUREcited by 4
  24. 2019Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensorcited by 3
  25. 2022HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral Denoising Packagecited by 3
  26. 2021Spectral Variability Augmented Sparse Unmixing of Hyperspectral Imagescited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 4.

12 papers.

  1. 2025HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagerycited by 17
  2. 2025A Survey on Archetypal Analysiscited by 6
  3. 2024MLXP: A Framework for Conducting Replicable Experiments in Pythoncited by 3
  4. 2025Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixingcited by 3
  5. 2026RamanPFN: learning from Raman spectral structure with a tabular foundation modelcited by 1
  6. 2024Fast Semisupervised Unmixing Using Nonconvex Optimizationcited by 0
  7. 2024Theoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspectivecited by 0
  8. 2025Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesiscited by 0
  9. 2025SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixingcited by 0
  10. 2026MiSiSUn: Minimum Simplex Semisupervised Unmixingcited by 0
  11. 2026Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioningcited by 0
  12. 2026Hyperspectral Unmixing Hierarchiescited by 0