Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples

Junshi Xia 1 Jocelyn Chanussot 1 Peijun Du 2 Xiyan He 1
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
Abstract : With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper.
Type de document :
Article dans une revue
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2015, pp.1-13. 〈10.1109/TGRS.2015.2481938〉
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http://hal.univ-grenoble-alpes.fr/hal-01246607
Contributeur : Vincent Couturier-Doux <>
Soumis le : vendredi 18 décembre 2015 - 17:38:13
Dernière modification le : lundi 9 avril 2018 - 12:22:35

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Junshi Xia, Jocelyn Chanussot, Peijun Du, Xiyan He. Rotation-Based Support Vector Machine Ensemble in Classification of Hyperspectral Data With Limited Training Samples. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2015, pp.1-13. 〈10.1109/TGRS.2015.2481938〉. 〈hal-01246607〉

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