dc.contributor.author |
Debba, Pravesh
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dc.contributor.author |
Cho, Moses A
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dc.contributor.author |
Mathieu, Renaud SA
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dc.date.accessioned |
2009-09-21T13:56:11Z |
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dc.date.available |
2009-09-21T13:56:11Z |
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dc.date.issued |
2009-08 |
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dc.identifier.citation |
Debba, P., Cho, M.A. 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 |
en |
dc.identifier.isbn |
978-1-4244-4948-4 |
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dc.identifier.uri |
http://hdl.handle.net/10204/3612
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dc.description |
Evolution in remote sensing. IEEE GRSS First Workshop on Hyperspectral Image and Signal, Grenoble, France, 26-28 August, 2009 |
en |
dc.description.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. |
en |
dc.language.iso |
en |
en |
dc.publisher |
IEEE |
en |
dc.subject |
Band selection |
en |
dc.subject |
Savannah trees |
en |
dc.subject |
Simulated annealing |
en |
dc.subject |
Stepwise discriminant analysis |
en |
dc.subject |
SDA |
en |
dc.subject |
Hyperspectral |
en |
dc.subject |
Spectral angle mapper |
en |
dc.subject |
SAM |
en |
dc.subject |
Spectral information divergence |
en |
dc.subject |
SID |
en |
dc.subject |
Remote sensing |
en |
dc.subject |
Electromagnetic spectrum |
en |
dc.subject |
Hyperspectral image |
en |
dc.subject |
Hyperspectral signal |
en |
dc.title |
Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level |
en |
dc.type |
Conference Presentation |
en |
dc.identifier.apacitation |
Debba, P., Cho, M. A., & Mathieu, R. S. (2009). Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level. IEEE. http://hdl.handle.net/10204/3612 |
en_ZA |
dc.identifier.chicagocitation |
Debba, Pravesh, Moses A Cho, and Renaud SA Mathieu. "Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level." (2009): http://hdl.handle.net/10204/3612 |
en_ZA |
dc.identifier.vancouvercitation |
Debba P, Cho MA, Mathieu RS, Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level; IEEE; 2009. http://hdl.handle.net/10204/3612 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Debba, Pravesh
AU - Cho, Moses A
AU - Mathieu, Renaud SA
AB - 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.
DA - 2009-08
DB - ResearchSpace
DP - CSIR
KW - Band selection
KW - Savannah trees
KW - Simulated annealing
KW - Stepwise discriminant analysis
KW - SDA
KW - Hyperspectral
KW - Spectral angle mapper
KW - SAM
KW - Spectral information divergence
KW - SID
KW - Remote sensing
KW - Electromagnetic spectrum
KW - Hyperspectral image
KW - Hyperspectral signal
LK - https://researchspace.csir.co.za
PY - 2009
SM - 978-1-4244-4948-4
T1 - Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level
TI - Optimal individual supervised hyperspectral band selection distinguishing savannah trees at leaf level
UR - http://hdl.handle.net/10204/3612
ER -
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en_ZA |