mlsanityData to 7 Oct 2026

Nonlinear unmixing of hyperspectral images: models and algorithms

arXiv 1304.1875 ↗First posted 6 Apr 2013physics.data-an stat.AP stat.MEcited by 27

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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.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2013.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 6; 2024: 6; 2025: 5; 2026: 3.

27 papers.

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  1. 2023Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Packagecited by 12
  2. 2021Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencodercited by 7
  3. 2021Using Low-rank Representation of Abundance Maps and Nonnegative Tensor Factorization for Hyperspectral Nonlinear Unmixingcited by 6
  4. 2021Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixingcited by 6
  5. 2022Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixingcited by 6
  6. 2024Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classificationcited by 4
  7. 2021Unrolling PALM for sparse semi-blind source separationcited by 3
  8. 2023AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoisingcited by 3
  9. 2024Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoderscited by 3
  10. 2025Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixingcited by 3
  11. 2023Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixingcited by 2
  12. 2024Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programmingcited by 2
15 more
  1. 2022GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothnesscited by 1
  2. 2023SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixingcited by 1
  3. 2025DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixingcited by 1
  4. 2021SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixingcited by 0
  5. 2023Approximation of radiative transfer for surface spectral featurescited by 0
  6. 2023Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoderscited by 0
  7. 2024Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learningcited by 0
  8. 2024Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Frameworkcited by 0
  9. 2024Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysiscited by 0
  10. 2025Hyper-spectral Unmixing algorithms for remote compositional surface mapping: a review of the state of the artcited by 0
  11. 2025SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixingcited by 0
  12. 2025Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximizationcited by 0
  13. 2026A Variational Latent-Space Framework for Uncertainty-Aware Spectral Image Emulationcited by 0
  14. 2026Looking into a Pixel by Nonlinear Unmixing -- A Generative Approachcited by 0
  15. 2026A Deep Equilibrium Network for Hyperspectral Unmixingcited by 0