KalacskaBohlmanSanchez-AzofeifaEtAl2007

Référence

Kalacska, M., Bohlman, S., Sanchez-Azofeifa, G.A., Castro-Esau, K., Caelli, T. (2007) Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels. Remote Sensing of Environment, 109(4):406-415. (Scopus )

Résumé

A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results followed by the wavelet energy feature vector and a form of principal component analysis. For the same dataset our results indicate that none of the pattern recognition classifiers performed the best across all reduction techniques, and also that none of the parametric classifiers had the overall lowest training and testing errors. At the crown level, in addition to higher testing error rates (7%), it was found that there was no optimal data reduction technique. The significant wavebands were also found to be different between the leaf and crown levels. At the leaf level, the visible region of the spectrum was the most important for discriminating between lianas and trees whereas at the crown level the shortwave infrared was also important in addition to the visible and near infrared. © 2007 Elsevier Inc. All rights reserved.

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@ARTICLE { KalacskaBohlmanSanchez-AzofeifaEtAl2007,
    AUTHOR = { Kalacska, M. and Bohlman, S. and Sanchez-Azofeifa, G.A. and Castro-Esau, K. and Caelli, T. },
    TITLE = { Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels },
    JOURNAL = { Remote Sensing of Environment },
    YEAR = { 2007 },
    VOLUME = { 109 },
    PAGES = { 406-415 },
    NUMBER = { 4 },
    ABSTRACT = { A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results followed by the wavelet energy feature vector and a form of principal component analysis. For the same dataset our results indicate that none of the pattern recognition classifiers performed the best across all reduction techniques, and also that none of the parametric classifiers had the overall lowest training and testing errors. At the crown level, in addition to higher testing error rates (7%), it was found that there was no optimal data reduction technique. The significant wavebands were also found to be different between the leaf and crown levels. At the leaf level, the visible region of the spectrum was the most important for discriminating between lianas and trees whereas at the crown level the shortwave infrared was also important in addition to the visible and near infrared. © 2007 Elsevier Inc. All rights reserved. },
    COMMENT = { Cited By (since 1996): 4 Export Date: 10 February 2010 Source: Scopus CODEN: RSEEA doi: 10.1016/j.rse.2007.01.012 },
    ISSN = { 00344257 (ISSN) },
    KEYWORDS = { Feature selection, Hyperspectral data, Panama, Pattern recognition, Tropical dry forest, Wavelet energy feature vector, Airborne telescopes, Data reduction, Error analysis, Feature extraction, Principal component analysis, Spectrum analysis, Wavelet analysis, Hyperspectral data, Parametric classifiers, Tropical dry forest, Wavelet energy feature vector, Forestry, canopy, comparative study, data set, dry forest, leaf, pattern recognition, principal component analysis, spectral analysis, wavelet, Data Processing, Forestry, Leaves, Central America, Panama [Central America] },
    OWNER = { Luc },
    TIMESTAMP = { 2010.02.10 },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-34347331344&partnerID=40&md5=dbc3e03926f533324294761471b2b9bf },
}

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