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,Andrew K Skidmore,Chaichoke Vaiphasa,Thomas A Groen,Martin Schlerf,Willem Nieuwenhuis

doi:10.3390/s120708755

Saleem Ullah, Andrew K Skidmore + Show 4 more

Open Access

https://doi.org/10.3390/s120708755

Copy DOI

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.

Highlights

Hyperspectral sensors, because of their high spectral detail over contiguous narrow bands, have proven to be a valuable tool for discriminating plants species [1,2,3,4] compared to multispectral resolution sensors [5]
The present study extends the genetic algorithms to the mid to thermal infrared for optimal band selection for discriminating plant species
This study has demonstrated the potential of genetic algorithms as band selectors using high resolution mid to thermal infrared emissivity spectra to differentiate between vegetation species at laboratory level

Summary

Introduction

Hyperspectral sensors, because of their high spectral detail over contiguous narrow bands, have proven to be a valuable tool for discriminating plants species [1,2,3,4] compared to multispectral resolution sensors [5]. Due to high dimensionality, working with hyperspectral data poses challenging problems such as redundancy, intensive computation, and singularity of covariance matrix inversion [6,7,8,9,10]. To overcome these problems, the dimensionality of hyperspectral data needs to be reduced without compromising the information content. Band selection is often preferred to band extraction as the physical meaning of the data remains unchanged [6,12,13,14,15]

Methods

Results

Discussion

Conclusion