Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform

Semih Dinç and Abdullah Bal

Mathematical Problems in Engineering, 2013, vol. 2013, 1-7

Abstract:

This paper presents a novel approach for the hyperspectral imagery (HSI) classification problem, using Kernel Fukunaga-Koontz Transform (K-FKT). The Kernel based Fukunaga-Koontz Transform offers higher performance for classification problems due to its ability to solve nonlinear data distributions. K-FKT is realized in two stages: training and testing. In the training stage, unlike classical FKT, samples are relocated to the higher dimensional kernel space to obtain a transformation from non-linear distributed data to linear form. This provides a more efficient solution to hyperspectral data classification. The second stage, testing, is accomplished by employing the Fukunaga- Koontz Transformation operator to find out the classes of the real world hyperspectral images. In experiment section, the improved performance of HSI classification technique, K-FKT, has been tested comparing other methods such as the classical FKT and three types of support vector machines (SVMs).

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:471915

DOI: 10.1155/2013/471915

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