Abstract
This paper presents a comparative evaluation between two classification strategies for the analysis of remote sensed data. The first is based on the combination of the outputs of a neural network (NN) ensemble, the second concerns the application of Support Vector Machine (SVM) classifiers. Emphasis is given to the understanding of the limits and the advantages of the two strategies to design a classifier system able to provide high generalization capability. Two sets of experiments have been carried out to classify benchmark remote sensed data. In the first set a Fuzzy Integral has been used to combine the outputs of neural classifiers in an ensemble. In the second set of experiments SVM classifiers have been trained and tested on the same data set. The comparative analysis evidences that SVM classifiers outperform an ensemble of classifiers, whose partial results are combined by a Fuzzy Integral. The training complexity of SVMs seems, however, to be a limitation to the extensive use of SVMs in complex multisource-multitemporal data classification.
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Tarantino, C. et al. (2006). Neural Network Ensemble and Support Vector Machine Classifiers: An Application to Remote Sensed Data. In: Di Gesú, V., Masulli, F., Petrosino, A. (eds) Fuzzy Logic and Applications. WILF 2003. Lecture Notes in Computer Science(), vol 2955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10983652_38
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DOI: https://doi.org/10.1007/10983652_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31019-8
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