TY - JOUR
T1 - Automated extraction of image-based endmember bundles for improved spectral unmixing
AU - Somers, Ben
AU - Zortea, MacIel
AU - Plaza, Antonio
AU - Asner, Gregory P.
N1 - Funding Information:
Manuscript received October 01, 2011; revised November 20, 2011; accepted December 13, 2011. Date of publication January 31, 2012; date of current version May 23, 2012. This work was supported by the Belgian Science Policy Office in the framework of the STEREO II programme—Project VEGEMIX (SR/67/146), by the European Community’s Marie Curie Research Training Networks Programme under reference MRTN-CT-2006-035927, Hyperspectral Imaging Network (HYPER-I-NET), by the Spanish Ministry of Science and Innovation (HYPERCOMP/EODIX project, reference AYA2008-05965-C04-02), and by the Junta de Extremadura (local government) under project PRI09A110.
Funding Information:
M. Zortea was a fellow of the Spanish “Juan de la Cierva” programme cofinanced by the European Social Fund. The Carnegie Airborne Observatory is supported by the W. M. Keck Foundation, Gordon and Betty Moore Foundation, and William Hearst III. The scientific input of Roberta Martin, Laurent Tits, David Knapp, and Prof. Pol Coppin is gratefully acknowledged. The authors acknowledge the two anonymous reviewers for their outstanding comments and suggestions, which greatly helped to improve the technical content and presentation of the manuscript.
PY - 2012
Y1 - 2012
N2 - Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to estimating the abundance of pure spectral constituents (endmembers) in each (possibly mixed) observation collected by the imaging instrument. In recent years, several endmember extraction algorithms (EEAs) have been proposed for automated endmember extraction from hyperspectral data sets. Traditionally, EEAs extract/select only one single standard endmember spectrum for each of the presented endmember classes or scene components. The use of fixed endmember spectra, however, is a simplification since in many cases the conditions of the scene components are spatially and temporally variable. As a result, variation in endmember spectral signatures is not always accounted for and, hence, spectral unmixing can lead to poor accuracy of the estimated endmember fractions. Here, we address this issue by developing a simple strategy to adapt available EEAs to select multiple endmembers (or bundles) per scene component. We run the EEAs in randomly selected subsets of the original hyperspectral image, and group the extracted samples of pure materials in a bundle using a clustering technique. The output is a spectral library of pure materials, extracted automatically from the input scene. The proposed technique is applied to several common EEAs and combined with an endmember variability reduction technique for unmixing purposes. Experiments with both simulated and real hyperspectral data sets indicate that the proposed strategy can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.
AB - Spectral unmixing is an important task in hyperspectral data exploitation. It amounts to estimating the abundance of pure spectral constituents (endmembers) in each (possibly mixed) observation collected by the imaging instrument. In recent years, several endmember extraction algorithms (EEAs) have been proposed for automated endmember extraction from hyperspectral data sets. Traditionally, EEAs extract/select only one single standard endmember spectrum for each of the presented endmember classes or scene components. The use of fixed endmember spectra, however, is a simplification since in many cases the conditions of the scene components are spatially and temporally variable. As a result, variation in endmember spectral signatures is not always accounted for and, hence, spectral unmixing can lead to poor accuracy of the estimated endmember fractions. Here, we address this issue by developing a simple strategy to adapt available EEAs to select multiple endmembers (or bundles) per scene component. We run the EEAs in randomly selected subsets of the original hyperspectral image, and group the extracted samples of pure materials in a bundle using a clustering technique. The output is a spectral library of pure materials, extracted automatically from the input scene. The proposed technique is applied to several common EEAs and combined with an endmember variability reduction technique for unmixing purposes. Experiments with both simulated and real hyperspectral data sets indicate that the proposed strategy can significantly improve fractional abundance estimations by accounting for endmember variability in the original hyperspectral data.
KW - Endmember extraction algorithms (EEAs)
KW - endmember variability
KW - hyperspectral imaging
KW - multiple endmember spectral mixture analysis (MESMA)
KW - spectral mixture analysis (SMA)
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U2 - 10.1109/JSTARS.2011.2181340
DO - 10.1109/JSTARS.2011.2181340
M3 - Article
AN - SCOPUS:84861741372
SN - 1939-1404
VL - 5
SP - 396
EP - 408
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
IS - 2
M1 - 6144017
ER -