Name
Title | ||
|---|---|---|
Kernel Fukunaga–Koontz Transform Subspaces for Classification of Hyperspectral Images With Small Sample Sizes |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LGRS.2015.2393438 | Geoscience and Remote Sensing Letters, IEEE |
Keywords | Field | DocType |
geophysical image processing,hyperspectral imaging,image classification,learning (artificial intelligence),object detection,remote sensing,support vector machines,wavelet transforms,fkt method,kfkt,higher dimensional space,hyperspectral data,hyperspectral image classification,kernel fukunaga-koontz transform subspace,nonlinear data separability,small sample size,supervised classification approach,support vector machine,target detection method,classification,fukunaga–koontz transform (fkt),fukunaga???koontz transform (fkt),hyperspectral images,kernel-based methods,vectors,learning artificial intelligence,kernel | Kernel (linear algebra),Small number,Computer vision,Nonlinear system,Pattern recognition,Clutter,Support vector machine,Linear subspace,Hyperspectral imaging,Artificial intelligence,Sample size determination,Mathematics | Journal |
Volume | Issue | ISSN |
12 | 6 | 1545-598X |
Citations | PageRank | References |
2 | 0.39 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamidullah Binol | 1 | 8 | 2.29 |
| Gökhan Bilgin | 2 | 62 | 13.18 |
| Semih Dinç | 3 | 13 | 3.47 |
| Abdullah Bal | 4 | 51 | 4.92 |