# Hyperspectral Unmixing Hierarchies

arXiv: https://arxiv.org/abs/2604.16969 · HTML: https://mlsanity.com/paper/2604.16969/

## 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.

## Builds on (most cited first, 15 of 15)

- [Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey](https://mlsanity.com/paper/1708.05125/) (2017)
- [Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms](https://mlsanity.com/paper/1805.10232/) (2018)
- [Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review](https://mlsanity.com/paper/2205.09933/) (2022)
- [Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry](https://mlsanity.com/paper/1904.03888/) (2019)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization](https://mlsanity.com/paper/1310.7441/) (2013)
- [Entropic Descent Archetypal Analysis for Blind Hyperspectral Unmixing](https://mlsanity.com/paper/2209.11002/) (2022)
- [Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF](https://mlsanity.com/paper/1408.2810/) (2014)
- [Dynamical spectral unmixing of multitemporal hyperspectral images](https://mlsanity.com/paper/1510.04238/) (2015)
- [A Gaussian mixture model representation of endmember variability in hyperspectral unmixing](https://mlsanity.com/paper/1710.00075/) (2017)
- [Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability](https://mlsanity.com/paper/1510.05893/) (2015)
- [Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data](https://mlsanity.com/paper/2104.02837/) (2021)
- [A consistent and flexible framework for deep matrix factorizations](https://mlsanity.com/paper/2206.10693/) (2022)
- [Unsupervised Post-Nonlinear Unmixing of Hyperspectral Images Using a Hamiltonian Monte Carlo Algorithm](https://mlsanity.com/paper/1304.2499/) (2013)
- [Variational Autoencoder Framework for Hyperspectral Retrievals (Hyper-VAE) of Phytoplankton Absorption and Chlorophyll a in Coastal Waters for NASA's EMIT and PACE Missions](https://mlsanity.com/paper/2504.13476/) (2025)

## 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.