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

Sensors

Saleem Ullah²  Thomas A. Groen²  Martin Schlerf¹  Andrew K. Skidmore²  Willem Nieuwenhuis² 

[1] Centre de Recherche Public-Gabriel Lippmann (CRPGL), L-4422 Belvaux, Luxembourg; E-Mail:;Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands; E-Mails:

关键词: genetic algorithms;    thermal infrared remote sensing;    spectral separability;    spectral emissivity;   

DOI  :  10.3390/s120708755

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.