mlsanityData to 14 Oct 2026

Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

arXiv 2001.07307 ↗First posted 21 Jan 2020eess.IV eess.SPcited by 46

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Abstract (arXiv)

The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of its results. Therefore, large efforts have been recently dedicated to mitigate the effects of spectral variability in SU. This resulted in the development of algorithms that incorporate different strategies to allow the EMs to vary within a hyperspectral image, using, for instance, sets of spectral signatures known a priori, Bayesian, parametric, or local EM models. Each of these approaches has different characteristics and underlying motivations. This paper presents a comprehensive literature review contextualizing both classic and recent approaches to solve this problem. We give a detailed evaluation of the sources of spectral variability and their effect in image spectra. Furthermore, we propose a new taxonomy that organizes existing works according to a practitioner's point of view, based on the necessary amount of supervision and on the computational cost they require. We also review methods used to construct spectral libraries (which are required by many SU techniques) based on the observed hyperspectral image, as well as algorithms for library augmentation and reduction. Finally, we conclude the paper with some discussions and an outline of possible future directions for the field.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jan 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 11; 2024: 11; 2025: 15; 2026: 4.

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  1. 2022Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Reviewcited by 13
  2. 2023Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clusteringcited by 13
  3. 2023DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classificationcited by 12
  4. 2023Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Packagecited by 12
  5. 2022Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixingcited by 11
  6. 2023Contrastive Multi-view Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networkscited by 11
  7. 2023Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networkscited by 8
  8. 2021Large-Scale Hyperspectral Image Clustering Using Contrastive Learningcited by 6
  9. 2023SUnAA: Sparse Unmixing using Archetypal Analysiscited by 5
  10. 2025Towards Lightweight Hyperspectral Image Super-Resolution with Depthwise Separable Dilated Convolutional Networkcited by 5
  11. 2022Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Imagescited by 4
  12. 2024Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilationcited by 4
34 more
  1. 2023AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoisingcited by 3
  2. 2025UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fieldscited by 3
  3. 2021Spectral Variability Augmented Sparse Unmixing of Hyperspectral Imagescited by 2
  4. 2023Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixingcited by 2
  5. 2024A Generalized Multiscale Bundle-Based Hyperspectral Sparse Unmixing Algorithmcited by 2
  6. 2024Sum-of-norms regularized Nonnegative Matrix Factorizationcited by 2
  7. 2024Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and Algorithmscited by 2
  8. 2024Classification of grapevine varieties using UAV hyperspectral imagingcited by 1
  9. 2024Weighted-Sum of Gaussian Process Latent Variable Modelscited by 1
  10. 2024Multiview Subspace Clustering of Hyperspectral Images based on Graph Convolutional Networkscited by 1
  11. 2025DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixingcited by 1
  12. 2025Hyperspectral Imagingcited by 1
  13. 2025Rethinking Coupled Tensor Analysis for Hyperspectral Super-Resolution: Recoverable Modeling Under Endmember Variabilitycited by 1
  14. 2023A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Littercited by 0
  15. 2023Robust retrieval of material chemical states in X-ray microspectroscopycited by 0
  16. 2023MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decompositioncited by 0
  17. 2024An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixingcited by 0
  18. 2024Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Frameworkcited by 0
  19. 2024Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysiscited by 0
  20. 2024Theoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspectivecited by 0
  21. 2025A Tractable Two-Step Linear Mixing Model Solved with Second-Order Optimization for Spectral Unmixing under Variabilitycited by 0
  22. 2025A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundlescited by 0
  23. 2025Hyperspectral Image Generation with Unmixing Guided Diffusion Modelcited by 0
  24. 2025Structural-Spectral Graph Convolution with Evidential Edge Learning for Hyperspectral Image Clusteringcited by 0
  25. 2025Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the artcited by 0
  26. 2025Phase-Locked SNR Band Selection for Weak Mineral Signal Detection in Hyperspectral Imagerycited by 0
  27. 2025Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performancecited by 0
  28. 2025Hyperspectral data augmentation with transformer-based diffusion modelscited by 0
  29. 2025Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximizationcited by 0
  30. 2025Diffusion Posterior Sampler for Hyperspectral Unmixing with Spectral Variability Modelingcited by 0
  31. 2026WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusioncited by 0
  32. 2026MiSiSUn: Minimum Simplex Semisupervised Unmixingcited by 0
  33. 2026A Deep Equilibrium Network for Hyperspectral Unmixingcited by 0
  34. 2026Robust blind unmixing: A geometric approach to overcoming basis variationcited by 0