Name
Title | ||
|---|---|---|
Hyperspectral image classification using fuzzy C-means based composite kernel approach. |
Abstract | ||
|---|---|---|
In the classification of high-dimensional hyperspectral images, only spectral information is not sufficient to obtain successful results when the number of training data is small. In this case, spatial information can be exploited as well as spectral information. For this purpose, we aimed to use spatial information obtained from the fuzzy C-means (FCM) algorithm and spectral information together with the help of composite kernels to classify hyperspectral images. The composite kernels obtained in experimental studies are used for classification purposes by using extreme learning machines (ELM) and support vector machines (SVM); in addition to that, the results were presented comparatively in the tables. |
Year | Venue | Keywords |
|---|---|---|
2017 | Signal Processing and Communications Applications Conference | Fuzzy c-means,extreme learning machine,composite kernels,support vector machines,spectral and spatial information |
Field | DocType | ISSN |
Hyperspectral image classification,Spatial analysis,Kernel (linear algebra),Training set,Data mining,Pattern recognition,Computer science,Fuzzy logic,Support vector machine,Hyperspectral imaging,Artificial intelligence,Statistical classification | Conference | 2165-0608 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ibrahim Onur Sigirci | 1 | 1 | 1.39 |
| Gökhan Bilgin | 2 | 62 | 13.18 |