Fast algorithms for robust hyperspectral endmember extraction based on worst-case simplex volume maximization

Hyperspectral endmember extraction (EE) is to estimate endmember signatures (or material spectra) from the hyperspectral data of an unexplored area for analyzing the materials and their composition therein. However, the presence of noise in the data posts a serious problem for EE. Recently, robustne...

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Bibliographische Detailangaben

Hauptverfasser:	Tsung-Han Chan, Ji-Yuan Liou, Ambikapathi, A., Wing-Kin Ma, Chong-Yung Chi
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Approximation algorithms Fast algorithms Hyperspectral images Hyperspectral imaging Noise Noise measurement Optimization Robust endmember extraction Robustness Simplex volume maximization Vectors
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