On endmember identification in hyperspectral images without pure pixels: a comparison of algorithms

DOI10.1007/s10851-011-0276-0zbMath1255.68263OpenAlexW2099703033MaRDI QIDQ1932910

Javier Plaza, Gabriel Martín, Antonio Plaza, Eligius M. T. Hendrix, Inmaculada F. García

Publication date: 22 January 2013

Published in: Journal of Mathematical Imaging and Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10851-011-0276-0

zbMATH Keywords

hyperspectral imagingspectral unmixingendmember extractionminimum enclosing simplex

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Computing methodologies for image processing (68U10)

Related Items (2)

NMF versus ICA for blind source separation ⋮ Regularized nonnegative matrix factorization: geometrical interpretation and application to spectral unmixing

Uses Software

• AS 136

Cites Work

• Algorithm AS 136: A K-Means Clustering Algorithm
• A lattice matrix method for hyperspectral image unmixing
• On the use of small training sets for neural network-based characterization of mixed pixels in remotely sensed hyperspectral images
• A Convex Analysis-Based Minimum-Volume Enclosing Simplex Algorithm for Hyperspectral Unmixing

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