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

2014-04-25
Özdemir, Okan Bilge
Çetin, Yasemin
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.

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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.