Abstract
The classification of hyperspectral imagery, using multiple classifier systems is discussed and an SVM-based ensemble is introduced. The data set is separated into separate feature subsets using the correlation between the different spectral bands as a criterion. Afterwards, each source is classified separately by an SVM classifier. Finally, the different outputs are used as inputs for final decision fusion that is based on an additional SVM classifier. The results using the proposed strategy are compared to classification results achieved by a single SVM and other well known classifier ensembles, such as random forests, boosting and bagging.
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Ceamanos, X., Waske, B., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R. (2009). Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_7
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DOI: https://doi.org/10.1007/978-3-642-02326-2_7
Publisher Name: Springer, Berlin, Heidelberg
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