Abstract
In this study, the performances of using parametric/ nonparametric regularized feature extractions and support vector machine for hyperspectral image classification is explored when the training sample size is small. The classification accuracies of RBF-based SVM using two feature extractions with three regularization techniques are evaluated. The results of two hyperspectral image classification experiments show that the performance of the combination of nonparametric weighted feature extraction and RBF-based SVM outperforms those of others.
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Kuo, BC., Chang, KY. (2005). Regularized Feature Extractions and Support Vector Machines for Hyperspectral Image Data Classification. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552413_125
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DOI: https://doi.org/10.1007/11552413_125
Publisher Name: Springer, Berlin, Heidelberg
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