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Proceedings Paper

Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction
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Paper Abstract

The performance of the kernel based techniques depends on the selection of kernel parameters. That’s why; suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

Paper Details

Date Published: 20 October 2015
PDF: 5 pages
Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460T (20 October 2015); doi: 10.1117/12.2197924
Show Author Affiliations
Hamidullah Binol, Yildiz Technical Univ. (Turkey)
Abdullah Bal, Yildiz Technical Univ. (Turkey)
Huseyin Cukur, Yildiz Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9646:
High-Performance Computing in Remote Sensing V
Bormin Huang D.D.S.; Sebastián López; Zhensen Wu; Jose M. Nascimento; Boris A. Alpatov; Jordi Portell de Mora, Editor(s)

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