SIAM Journal on Imaging Sciences· 2026Q1
Hyperspectral Image Data Reduction for Endmember Extraction
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- Q1SCImago
- 2026year
Short summary
A new data reduction technique for hyperspectral images significantly cuts computational time for endmember extraction without losing accuracy, by removing mixed pixels while preserving pure ones.
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Key points
- Developed a data reduction technique for hyperspectral images based on the linear mixing model and pure-pixel assumption.
- The technique removes mixed pixels and preserves pixels close to pure endmember signatures.
- Integrated data reduction with a self-dictionary method using linear programming.
- Numerical experiments show substantial reduction in computational time without loss of endmember extraction accuracy.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract. 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.
The authors' abstract, as published at the source. SIAM Journal on Imaging Sciences, 2026 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering