Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform


Dinc S. , Bal A.

MATHEMATICAL PROBLEMS IN ENGINEERING, 2013 (SCI İndekslerine Giren Dergi)

  • Basım Tarihi: 2013
  • Doi Numarası: 10.1155/2013/471915
  • Dergi Adı: MATHEMATICAL PROBLEMS IN ENGINEERING

Özet

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).