The Effect of Training Data on Hyperspectral Classification Algorithms

Date

2013-01-01

Author

Özdemir, Okan Bilge
Cetin, Yasemin Yardimci

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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.

Subject Keywords

Hyperspectral Classification, Support Vector Machines, Maximum Likelihood, K-Nearest Neighbor

URI

https://hdl.handle.net/11511/57400

DOI

https://doi.org/10.1109/siu.2013.6531323

Conference Name

21st Signal Processing and Communications Applications Conference (SIU)

Collections

Graduate School of Informatics, Conference / Seminar

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Citation Formats

O. B. Özdemir and Y. Y. Cetin, “The Effect of Training Data on Hyperspectral Classification Algorithms,” presented at the 21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57400.