dc.contributor.author |
Debba, Pravesh
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|
dc.date.accessioned |
2010-04-13T07:39:17Z |
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dc.date.available |
2010-04-13T07:39:17Z |
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dc.date.issued |
2009-07 |
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dc.identifier.citation |
Debba, P 2009. Abundance estimation of spectrally similar minerals. IEEE, International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009, pp 1-4 |
en |
dc.identifier.isbn |
978-1-4244-3395-7 |
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dc.identifier.uri |
http://hdl.handle.net/10204/4019
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dc.description |
IEEE, International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009 |
en |
dc.description.abstract |
This paper evaluates a spectral unmixing method for estimating the partial abundance of spectrally similar minerals in complex mixtures. The method requires formulation of a linear function of individual spectra of individual minerals. The first and second derivatives of each of the different sets of mixed spectra and the individual spectra are determined, at signal-to-noise ratios of 50:1, 200:1 and 500:1. The error is minimized by means of simulated annealing. Experiments were made on several different mixtures of selected end-members, which could plausibly occur in real situations. The authors conclude that in the method proposed, the use of the original and first order derivatives provides a valuable contribution to unmixing procedures provided the signal-to-noise ratio is between 50:1 and 200:1. When the signal-to-noise ratio increases, the second derivative of the observed spectrum and the second derivatives of the end-member spectra give most precise estimates for the partial abundance of each end-member. This can often be seen when the signal-to-noise ratio is of the order 500:1. |
en |
dc.language.iso |
en |
en |
dc.publisher |
IEEE |
en |
dc.subject |
Remote sensing |
en |
dc.subject |
Spectral unmixing |
en |
dc.subject |
Derivatives |
en |
dc.subject |
Signal-to-noise ratio |
en |
dc.subject |
Geoscience |
en |
dc.subject |
Hyperspectral |
en |
dc.title |
Abundance estimation of spectrally similar minerals |
en |
dc.type |
Conference Presentation |
en |
dc.identifier.apacitation |
Debba, P. (2009). Abundance estimation of spectrally similar minerals. IEEE. http://hdl.handle.net/10204/4019 |
en_ZA |
dc.identifier.chicagocitation |
Debba, Pravesh. "Abundance estimation of spectrally similar minerals." (2009): http://hdl.handle.net/10204/4019 |
en_ZA |
dc.identifier.vancouvercitation |
Debba P, Abundance estimation of spectrally similar minerals; IEEE; 2009. http://hdl.handle.net/10204/4019 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Debba, Pravesh
AB - This paper evaluates a spectral unmixing method for estimating the partial abundance of spectrally similar minerals in complex mixtures. The method requires formulation of a linear function of individual spectra of individual minerals. The first and second derivatives of each of the different sets of mixed spectra and the individual spectra are determined, at signal-to-noise ratios of 50:1, 200:1 and 500:1. The error is minimized by means of simulated annealing. Experiments were made on several different mixtures of selected end-members, which could plausibly occur in real situations. The authors conclude that in the method proposed, the use of the original and first order derivatives provides a valuable contribution to unmixing procedures provided the signal-to-noise ratio is between 50:1 and 200:1. When the signal-to-noise ratio increases, the second derivative of the observed spectrum and the second derivatives of the end-member spectra give most precise estimates for the partial abundance of each end-member. This can often be seen when the signal-to-noise ratio is of the order 500:1.
DA - 2009-07
DB - ResearchSpace
DP - CSIR
KW - Remote sensing
KW - Spectral unmixing
KW - Derivatives
KW - Signal-to-noise ratio
KW - Geoscience
KW - Hyperspectral
LK - https://researchspace.csir.co.za
PY - 2009
SM - 978-1-4244-3395-7
T1 - Abundance estimation of spectrally similar minerals
TI - Abundance estimation of spectrally similar minerals
UR - http://hdl.handle.net/10204/4019
ER -
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en_ZA |