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

Hamidullah Binol; Abdullah Bal; Huseyin Cukur

doi:10.1117/12.2197924

20 October 2015 Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction

Hamidullah Binol, Abdullah Bal, Huseyin Cukur

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Proceedings Volume 9646, High-Performance Computing in Remote Sensing V; 96460T (2015) https://doi.org/10.1117/12.2197924
Event: SPIE Remote Sensing, 2015, Toulouse, France

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.

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Hamidullah Binol, Abdullah Bal, and Huseyin Cukur "Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction", Proc. SPIE 9646, High-Performance Computing in Remote Sensing V, 96460T (20 October 2015); https://doi.org/10.1117/12.2197924

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