Kernel Fukunaga-Koontz Transform Subspaces for Classification of Hyperspectral Images With Small Sample Sizes

In this letter, a novel supervised classification approach is presented for the classification of hyperspectral images using kernel Fukunaga-Koontz transform (KFKT). The Fukunaga-Koontz transform (FKT) is originally a powerful target detection method used in remote sensing tasks, and it is an especi...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2015-06, Vol.12 (6), p.1287-1291
Main Authors: Binol, Hamidullah, Bilgin, Gokhan, Dinc, Semih, Bal, Abdullah
Format: Article
Language:English
Subjects:
Classification
Covariance matrices
Fukunaga-Koontz transform (FKT)
hyperspectral images
Hyperspectral imaging
Kernel
kernel-based methods
Remote sensing
Training
Transforms
Vectors
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Summary:In this letter, a novel supervised classification approach is presented for the classification of hyperspectral images using kernel Fukunaga-Koontz transform (KFKT). The Fukunaga-Koontz transform (FKT) is originally a powerful target detection method used in remote sensing tasks, and it is an especially good classification tool for two-class problems. The traditional FKT method has been kernelized for increasing the nonlinear discrimination ability and capturing higher order of statistics of data. The proposed approach in this letter aims to solve the multiclass problem by regarding one class as target that is tried to be separated from the remaining classes (as clutter) like one-against-all methodology. The KFKT provides superior performance in the classification of hyperspectral data even using small number of samples because of nonlinear separability of data in higher dimensional space. The experiments confirm that KFKT has better and promising results than FKT and support vector machine in classification of hyperspectral images.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2015.2393438