Paper Title:
Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection
  Abstract

The great number of captured near spectral bands in hyperspectral images causes the curse of dimensionality problem and results in low classification accuracy. The feature selection algorithms try to overcome this problem by limiting the input space dimensions of classification for hyperspectral images. In this paper, immune clonal selection optimization algorithm is used for feature selection. Also one of the fastest Artificial Immune classification algorithms is used to compute fitness function of the feature selection. The comparison of the feature selection results with genetic algorithm shows the clonal selection’s higher performance to solve selection of features.

  Info
Periodical
Chapter
Chapter 2: Microwaves Optics and Image
Edited by
David Wang
Pages
799-805
DOI
10.4028/www.scientific.net/KEM.500.799
Citation
F. Samadzadegan, S. R. Namin, M. A. Rajabi, "Evaluating the Potential of Clonal Selection Optimization Algorithm to Hyperspectral Image Feature Selection", Key Engineering Materials, Vol. 500, pp. 799-805, 2012
Online since
January 2012
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