Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation

Date

2014-04-25

Author

Özdemir, Okan Bilge
Çetin, Yasemin

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In this study, the performance of hyperspectral classification algorithms with limited training data investigated. Support Vector Machines (SVM) with Gaussian kernel is used. Principle Component Analysis (PCA) is employed for preprocessing and meanshift segmentation is used to incorporate spatial information with spectral information to observe the effect spatial information. Pattern search algorithm is used to optimize meanshift segmentation parameters. The performance of the algorithm is demonstrated on high resolution Pavia University hyperspectral data.

Subject Keywords

Hyperspectral classification, Support vector machines, Meanshift segmentation, Pattern search

URI

https://hdl.handle.net/11511/54754

Conference Name

22nd IEEE 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. Çetin, “Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation,” presented at the 22nd IEEE Signal Processing and Communications Applications Conference (SIU), Karadeniz Teknik Univ, Trabzon, TURKEY, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54754.