Improvement of hyperspectral classification accuracy with limited training data using meanshift segmentation
| Autor: | Yasemin Yardimci Cetin, Okan Bilge Ozdemir |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Computer science
business.industry Hyperspectral imaging Pattern recognition Support vector machine symbols.namesake Statistical classification Computer Science::Computer Vision and Pattern Recognition Principal component analysis Gaussian function symbols Computer vision Segmentation Artificial intelligence business Spatial analysis |
| Zdroj: | SIU |
| DOI: | 10.1109/siu.2014.6830599 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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