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Title: A weighted linear spectral mixture analysis approach to address endmember variability in agricultural production systems
Authors: Somers, Ben ×
Delalieux, Stephanie
Stuckens, Jan
Verstraeten, Willem
Coppin, Pol #
Issue Date: Jan-2009
Publisher: Taylor & Francis
Series Title: International Journal of Remote Sensing vol:30 issue:1 pages:139-147
Abstract: The least squares error (LSE) technique is frequently used to estimate abundance fractions in linear spectral mixture analysis (LSMA). The LSE is typically equally weighted for all wavebands, assuming equally important effects. This is, however,not always the case and therefore traditional LSMA often results in suboptimal fraction estimates. This study presents a weighted LSMA approach that prioritises wavebands with minor or no negative effects on fraction estimates. Synthetic mixed pixel spectra compiled from in situ measured spectra of bare soil, citrus tree and weed canopies were used for validation. The results show
markedly improved fraction estimates obtained for the weighted approach, with a mean absolute gain of 0.24 in R2 and a mean absolute reduction in fraction abundance error of 0.06.
ISSN: 0143-1161
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Division M3-BIORES: Measure, Model & Manage Bioresponses (-)
× corresponding author
# (joint) last author

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