Fusion of support vector machines for classification of multisensor data

Rannsóknarafurð: Framlag til fræðitímaritsGreinritrýni

315 Tilvitnanir (Scopus)

Útdráttur

The classification of multisensor data sets, consisting of multitemporal synthetic aperture radar data and optical imagery, is addressed. The concept is based on the decision fusion of different outputs. Each data source is treated separately and classified by a support vector machine (SVM). Instead of fusing the final classification outputs (i.e., land cover classes), the original outputs of each SVM discriminant function are used in the subsequent fusion process. This fusion is performed by another SVM, which is trained on the a priori outputs. In addition, two voting schemes are applied to create the final classification results. The results are compared with well-known parametric and nonparametric classifier methods, i.e., decision trees, the maximumlikelihood classifier, and classifier ensembles. The proposed SVM-based fusion approach outperforms all other approaches and significantly improves the results of a single SVM, which is trained on the whole multisensor data set.

Upprunalegt tungumálEnska
Síður (frá-til)3858-3866
Síðufjöldi9
FræðitímaritIEEE Transactions on Geoscience and Remote Sensing
Bindi45
Númer tölublaðs12
DOI
ÚtgáfustaðaÚtgefið - des. 2007

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