Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images

@article{Demir2009ClusteringBasedEO,
  title={Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images},
  author={Beg{\"u}m Demir and Sarp Ert{\"u}rk},
  journal={IEEE Geoscience and Remote Sensing Letters},
  year={2009},
  volume={6},
  pages={840-844}
}
This letter presents an accurate support vector machine (SVM)-based hyperspectral image classification algorithm, which uses border training patterns that are close to the separating hyperplane. Border training patterns are obtained in two consecutive steps. In the first step, clustering is performed to training data of each class, and cluster centers are taken as initial training data for SVM. In the second step, the reduced-size training data composed of cluster centers are used in SVM… CONTINUE READING

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