Abstract
The classification of hyperspectral data is addressed using a classifier ensemble based on Support Vector Machines (SVM). First of all, the hyperspectral data set is decomposed into few sources according to the spectral bands correlation. Then, each source is treated separately and classified by an SVM classifier. Finally, all outputs are used as inputs for the final decision fusion, performed by an additional SVM classifier. The results of experiments, clearly show that the proposed SVM-based decision fusion outperforms a single SVM classifier in terms of overall accuracies.
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