REDUCED COMPUTATIONAL COMPLEXITY OF HYPER SPECTRAL IMAGE USING HYBRID INTELLIGENCE
G. Kaviya
U.G.Student, Department of ECE, IFET College of Engineering, Villupuram – 605 108
C. Sundhar
Associate Professor, Department of ECE, IFET College of Engineering, Villupuram – 605 108
59-69
Vol: 4, Issue: 4, 2014
Receiving Date:
2014-10-09
Acceptance Date:
2014-11-10
Publication Date:
2014-12-09
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Abstract
Hyper spectral data contains both spatial and spectral information by hyper spectral sensors in
remote sensing application. In this proposed method, hybrid intelligence is widely used to reduce
the overall accuracy. In that, Artificial neural networks (ANNs) are used to address the
dimensionality issues. Major problem of computation complexity of hyper spectral image can be
reduced by using Particle Swarm Optimization (PSO) algorithm and Knowledge encoded Genetic
Algorithm (KE-GA).The proposed model thus explores jointly the advantages of ANNs and Particle
Swarm Optimization (PSO). This hybrid intelligence is justified in land covers classification of HSI
images acquired by different remotely placed sensors.
Keywords:
Hyper spectral data, Artificial neural networks (ANNs), Particle swarm optimization (PSO) , Knowledge encoded Genetic Algorithm (KEGN).
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