Hyperspectral Image Classification Using Kernel Fukunaga-Koontz Transform
MATHEMATICAL PROBLEMS IN ENGINEERING, 2013 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Publication Date: 2013
- Doi Number: 10.1155/2013/471915
- Journal Name: MATHEMATICAL PROBLEMS IN ENGINEERING
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Open Archive Collection: AVESIS Open Access Collection
- Yıldız Technical University Affiliated: Yes
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).