A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data

Xavier Ceamanos*, Björn Waske, Jón Atli Benediktsson, Jocelyn Chanussot, Mathieu Fauvel, Johannes R. Sveinsson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

87 Citations (Scopus)

Abstract

Classification of hyperspectral data using a classifier ensemble that is based on support vector machines (SVMs) are addressed. First, the hyperspectral data set is decomposed into a few data sources according to the similarity of the spectral bands. Then, each source is processed separately by performing classification based on SVM. Finally, all outputs are used as input for final decision fusion performed by an additional SVM classifier. Results of the experiments underline how the proposed SVM fusion ensemble outperforms a standard SVM classifier in terms of overall and class accuracies, the improvement being irrespective of the size of the training sample set. The definition of the data sources resulting from the original data set is also studied.

Original languageEnglish
Pages (from-to)293-307
Number of pages15
JournalInternational Journal of Image and Data Fusion
Volume1
Issue number4
DOIs
Publication statusPublished - Dec 2010

Bibliographical note

Funding Information:
This research was partially supported by the Research Fund of the University of Iceland.

Other keywords

  • Classification of remote sensing data
  • Decision fusion
  • Hyperspectral data
  • Support vector machines

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