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Hyperspectral Unmixing Hierarchies

arXiv 2604.16969 ↗First posted 18 Apr 2026cs.CV eess.IVcited by 0

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Abstract (arXiv)

Unmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth requirements, can accommodate mixed pixels, and is closely tied to light propagation, it is a uniquely powerful tool for analyzing hyperspectral images. However, spectral variability inhibits unmixing performance, the proper way to determine the number of endmembers is ambiguous, and the clarity of the endmembers degrades as more are included. Hierarchical structure is a possible solution to all three problems. Here, hierarchical unmixing is defined by imposing a hierarchical abundance sum constraint on Deep Nonnegative Matrix Factorization. Binary Linear Unmixing Tactile Hierarchies (BLUTHs) solve the hierarchical unmixing problem with a simple network architecture. Sparsity modulation unmixing growth tailors the topology of a BLUTH to each scene. The structure imposed by BLUTHs allows endmembers with varying levels of spectral contrast to be revealed, mitigating the challenge of spectral variability. The performance of BLUTHs exceeds state-of-the-art unmixing algorithms on laboratory scenes, particularly with regard to abundance estimation, while their performance remains competitive on remote sensing scenes. In addition, ocean color unmixing by BLUTHs is demonstrated on hyperspectral scenes from the HYPSO and PACE satellites.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 88 references that are in the map. First posted on arXiv 18 Apr 2026.

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15 of 88 references.

References · 15

  1. 2017Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Surveycited by 33
  2. 2018Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Normscited by 14
  3. 2022Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Reviewcited by 13
  4. 2019Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometrycited by 12
  5. 2023Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Packagecited by 12
  6. 2013Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorizationcited by 11
  7. 2022Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixingcited by 11
  8. 2014Spectral Unmixing of Hyperspectral Imagery using Multilayer NMFcited by 9
  9. 2015Dynamical spectral unmixing of multitemporal hyperspectral imagescited by 9
  10. 2017A Gaussian mixture model representation of endmember variability in hyperspectral unmixingcited by 7
  11. 2015Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variabilitycited by 6
  12. 2021Fast Unmixing and Change Detection in Multitemporal Hyperspectral Datacited by 4
3 more
  1. 2022A consistent and flexible framework for deep matrix factorizationscited by 3
  2. 2013Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithmcited by 2
  3. 2025Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA's EMIT and PACE Missionscited by 2

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