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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/3612

Title: Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level
Authors: Debba, P
Cho, M
Mathieu, R
Keywords: Band selection
Savannah trees
Simulated annealing
Stepwise discriminant analysis
SDA
Hyperspectral
Spectral angle mapper
SAM
Spectral information divergence
SID
Remote sensing
Electromagnetic spectrum
Hyperspectral image
Hyperspectral signal
Issue Date: Aug-2009
Publisher: IEEE
Citation: Debba, P, Cho, M and Mathieu, R. 2009. Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level. Evolution in remote sensing. IEEE GRSS First Workshop on Hyperspectral Image and Signal, Grenoble, France, 26-28 August, 2009. pp 1-4
Abstract: This paper uses simulated annealing and focus on the spectral angle mapper (SAM), to demonstrate how the separability of two mean spectra from different species can be increased by choosing the bands that maximize the metric. It is known that classification performance is enhanced when the differences in mean spectra for each endmember species are maximized. Comparison was made using the selected bands derived from the proposed method, to all bands in the electromagnetic spectrum (EMS), only the bands in the visible, near infrared and short wave infrared regions of the EMS and selected bands using stepwise discriminant analysis. The bands from the proposed method often indicates a better choice of band selection as viewed by the summary statistics for (a) the SAM measurements, (b) the correlations between bands and (c) the spectral information divergence (SID), for each pair of species; and the classification accuracy of SAM and SID.
Description: Evolution in remote sensing. IEEE GRSS First Workshop on Hyperspectral Image and Signal, Grenoble, France, 26-28 August, 2009
URI: http://hdl.handle.net/10204/3612
ISBN: 978-1-4244-4948-4
Appears in Collections:Logistics and quantitative methods
General science, engineering & technology

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