# Hyperspectral Image Data Reduction for Endmember Extraction

arXiv: https://arxiv.org/abs/2512.10506 · HTML: https://mlsanity.com/paper/2512.10506/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 11 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Nonnegative Matrix Factorization for Signal and Data Analytics: Identifiability, Algorithms, and Applications](https://mlsanity.com/paper/1803.01257/) (2018)
- [Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey](https://mlsanity.com/paper/1708.05125/) (2017)
- [Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms](https://mlsanity.com/paper/1909.12325/) (2019)
- [Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation](https://mlsanity.com/paper/1310.7529/) (2013)
- [Blind Audio Source Separation with Minimum-Volume Beta-Divergence NMF](https://mlsanity.com/paper/1907.02404/) (2019)
- [Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization](https://mlsanity.com/paper/1310.2273/) (2013)
- [Robust Near-Separable Nonnegative Matrix Factorization Using Linear Optimization](https://mlsanity.com/paper/1302.4385/) (2013)
- [A Fast Gradient Method for Nonnegative Sparse Regression with Self Dictionary](https://mlsanity.com/paper/1610.01349/) (2016)
- [Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability](https://mlsanity.com/paper/1309.5701/) (2013)
- [Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach](https://mlsanity.com/paper/2109.11135/) (2021)
- [Refinement of Hottopixx Method for Nonnegative Matrix Factorization Under Noisy Separability](https://mlsanity.com/paper/2109.02863/) (2021)
- [Robustness Analysis of Preconditioned Successive Projection Algorithm for General Form of Separable NMF Problem](https://mlsanity.com/paper/1506.08387/) (2015)
- [Robust Vertex Enumeration for Convex Hulls in High Dimensions](https://mlsanity.com/paper/1802.01515/) (2018)
- [Endmember Extraction from Hyperspectral Images Using Self-Dictionary Approach with Linear Programming](https://mlsanity.com/paper/2404.13098/) (2024)
- [Combinatorial Redundancy Detection](https://mlsanity.com/paper/1412.1241/) (2014)
- [Convex Programming Based Spectral Clustering](https://mlsanity.com/paper/1805.04246/) (2018)

## Abstract (arXiv)

Endmember extraction from hyperspectral images aims to identify the spectral signatures of materials present in a scene. Recent studies have shown that self-dictionary methods can achieve high extraction accuracy; however, their high computational cost limits their applicability to large-scale hyperspectral images. Although several approaches have been proposed to mitigate this issue, it remains a major challenge. Motivated by this situation, this paper pursues a data reduction approach. Assuming that a hyperspectral image follows the linear mixing model with the pure-pixel assumption, we develop a data reduction technique to remove pixels corresponding to mixtures of multiple endmember signatures. We analyze the theoretical properties of this reduction step and show that it preserves pixels that lie close to the endmembers. Building on this result, we propose a data-reduced self-dictionary method that integrates the data reduction with a self-dictionary method based on a linear programming formulation. Numerical experiments demonstrate that the proposed method can substantially reduce the computational time of the original self-dictionary method without sacrificing endmember extraction accuracy.