Feature Selection Based on High Dimensional Model Representation for Hyperspectral Images

In hyperspectral image analysis, the classification task has generally been addressed jointly with dimensionality reduction due to both the high correlation between the spectral features and the noise present in spectral bands, which might significantly degrade classification performance. In supervi...

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Veröffentlicht in:	IEEE transactions on image processing 2017-06, Vol.26 (6), p.2918-2928
Hauptverfasser:	Taskin, Gulsen, Kaya, Huseyin, Bruzzone, Lorenzo
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational efficiency Computational modeling Correlation Dimensionality reduction Feature extraction feature selection high dimensional model representation hyperspectral image classification Hyperspectral imaging Kernel Training
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