期刊
INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION
卷 1, 期 4, 页码 293-307出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/19479832.2010.485935
关键词
classification of remote sensing data; support vector machines; hyperspectral data; decision fusion
资金
- Research Fund of the University of Iceland
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.
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