Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species

Saleem Ullah*, Thomas A. Groen, Martin Schlerf, Andrew K. Skidmore, Willem Nieuwenhuis, Chaichoke Vaiphasa

*Corresponding author for this work

Research output: Contribution to journalArticle

30 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)8755-8769
Number of pages15
JournalSensors
Volume12
Issue number7
DOIs
Publication statusPublished - 2012
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2012. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • genetic algorithms
  • thermal infrared remote sensing
  • spectral separability
  • spectral emissivity

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