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Using a Genetic Algorithm as an Optimal Band Selector in the Mid and Thermal Infrared (2.5–14 µm) to Discriminate Vegetation Species
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
Centre de Recherche Public-Gabriel Lippmann (CRPGL), L-4422 Belvaux, Luxembourg
Department of Survey Engineering, Faculty of Engineer, Chulalongkorn University, 10330 Bangkok, Thailand
* Author to whom correspondence should be addressed.
Received: 15 May 2012; in revised form: 13 June 2012 / Accepted: 15 June 2012 / Published: 27 June 2012
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
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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.
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.
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.