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Improvement of Hyperspectral Classification Accuracy with Limited Training Data Using Meanshift Segmentation
Date
2014-04-25
Author
Özdemir, Okan Bilge
Çetin, Yasemin
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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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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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.