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Sensors 2012, 12(7), 8755-8769; doi:10.3390/s120708755
Article

Using a Genetic Algorithm as an Optimal Band Selector in the Mid and Thermal Infrared (2.5–14 µm) to Discriminate Vegetation Species

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Received: 15 May 2012; in revised form: 13 June 2012 / Accepted: 15 June 2012 / Published: 27 June 2012
(This article belongs to the Section Remote Sensors)
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Abstract: Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.
Keywords: genetic algorithms; thermal infrared remote sensing; spectral separability; spectral emissivity genetic algorithms; thermal infrared remote sensing; spectral separability; spectral emissivity
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Ullah, S.; Groen, T.A.; Schlerf, M.; Skidmore, A.K.; Nieuwenhuis, W.; Vaiphasa, C. Using a Genetic Algorithm as an Optimal Band Selector in the Mid and Thermal Infrared (2.5–14 µm) to Discriminate Vegetation Species. Sensors 2012, 12, 8755-8769.

AMA Style

Ullah S, Groen TA, Schlerf M, Skidmore AK, Nieuwenhuis W, Vaiphasa C. Using a Genetic Algorithm as an Optimal Band Selector in the Mid and Thermal Infrared (2.5–14 µm) to Discriminate Vegetation Species. Sensors. 2012; 12(7):8755-8769.

Chicago/Turabian Style

Ullah, Saleem; Groen, Thomas A.; Schlerf, Martin; Skidmore, Andrew K.; Nieuwenhuis, Willem; Vaiphasa, Chaichoke. 2012. "Using a Genetic Algorithm as an Optimal Band Selector in the Mid and Thermal Infrared (2.5–14 µm) to Discriminate Vegetation Species." Sensors 12, no. 7: 8755-8769.



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