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A Customized Particle Swarm Optimization for Classification of Multispectral Imagery Based on Feature Fusion

2008
Journal from gdlhub / 2017-08-14 11:52:31
Oleh : Venkatalakshmi Krishnan, Anisha Praisy, Maragathavalli R, Mercy Shalinie, IAJIT
Dibuat : 2012-06-22, dengan 1 file

Keyword : Multispectral image, decision based feature extraction, particle swam optimization, global optima, g-best.
Subjek : A Customized Particle Swarm Optimization for Classification of Multispectral Imagery Based on Feature Fusion
Url : http://www.ccis2k.org/iajit/PDF/vol.5,no.4/1-178.pdf
Sumber pengambilan dokumen : Internet

An attempt has been made in this paper to classify multispectral images using customized particle swam


optimization. To reduce the time consumption due to increase in dimensionality of multispectral imagery a preprocessing is


done using feature extraction based on decision boundary. The customized particle swam optimization then works on the


reduced multispectral imagery to find globally optimal cluster centers. Here particle swam optimization is tailored for


classification of multispectral images as customized particle swam optimization. The modifications are performed on the


velocity function such that velocity in each iteration is updated as a factor of g-best (global best) alone and the particle


structure is made to incorporate the entire cluster centers of the reduced imagery. The initialization of particles is


accomplished using modified k-means in order to retain the simplicity. AVIRIS images are used as test site and it was found


that the customized particle swam optimization finds the globally optimal clusters with 98.56% accuracy.

Deskripsi Alternatif :

An attempt has been made in this paper to classify multispectral images using customized particle swam


optimization. To reduce the time consumption due to increase in dimensionality of multispectral imagery a preprocessing is


done using feature extraction based on decision boundary. The customized particle swam optimization then works on the


reduced multispectral imagery to find globally optimal cluster centers. Here particle swam optimization is tailored for


classification of multispectral images as customized particle swam optimization. The modifications are performed on the


velocity function such that velocity in each iteration is updated as a factor of g-best (global best) alone and the particle


structure is made to incorporate the entire cluster centers of the reduced imagery. The initialization of particles is


accomplished using modified k-means in order to retain the simplicity. AVIRIS images are used as test site and it was found


that the customized particle swam optimization finds the globally optimal clusters with 98.56% accuracy.

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