The effect of training data on hyperspectral classification algorithms

In this study, the performance of different hyperspectral classification algorithms with the same training set is investigated. In addition, the effect of the dimension and sampling strategy for the training set selection is demonstrated. Support Vector Machines (SVM), K- Nearest Neighbor (K-NN) and...

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Bibliographic Details
Published in2013 21st Signal Processing and Communications Applications Conference (SIU) pp. 1 - 4
Main Authors Ozdemir, Okan Bilge, Cetin, Y. Y.
Format Conference Proceeding
LanguageEnglish
Turkish
Published IEEE 01.04.2013
Subjects
Classification algorithms
Hyperspectral Classification
Hyperspectral imaging
K-Nearest Neighbor
Kernel
Maximum Likelihood
Support vector machines
Training
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Summary:In this study, the performance of different hyperspectral classification algorithms with the same training set is investigated. In addition, the effect of the dimension and sampling strategy for the training set selection is demonstrated. Support Vector Machines (SVM), K- Nearest Neighbor (K-NN) and Maximum Likelihood (ML) methods are used. The contribution of using spatial information with spectral information is observed. Meanshift segmentation and window weighting methods are used for spatial information. High resolution Pavia University hyperspectral data and Indian Pines data are used in this study.
ISBN:9781467355629
1467355623
DOI:10.1109/SIU.2013.6531323