Improving Random Forest With Ensemble of Features and Semisupervised Feature Extraction

Junshi Xia 1 Wenzhi Liao 2 Jocelyn Chanussot 1 Peijun Du 3 Guanghan Song 4 W. Philips
1 GIPSA-SIGMAPHY - SIGMAPHY
GIPSA-DIS - Département Images et Signal
4 GIPSA-SAIGA - SAIGA
GIPSA-DA - Département Automatique, GIPSA-DIS - Département Images et Signal
Abstract : In this letter, we propose a novel approach for improving Random Forest (RF) in hyperspectral image classification. The proposed approach combines the ensemble of features and the semisupervised feature extraction (SSFE) technique. The main contribution of our approach is to construct an ensemble of RF classifiers. In this way, the feature space is divided into several disjoint feature subspaces. Then, the feature subspaces induced by the SSFE technique are used as the input space to an RF classifier. This method is compared with a regular RF and an RF with the reduced features by the SSFE on two real hyperspectral data sets, showing an improved performance in ill-posed, poor-posed, and well-posed conditions. An additional study shows that the proposed method is less sensitive to the parameters.
Type de document :
Article dans une revue
IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2015, 12 (7), pp.1471 - 1475. 〈10.1109/LGRS.2015.2409112〉
Liste complète des métadonnées

http://hal.univ-grenoble-alpes.fr/hal-01246585
Contributeur : Vincent Couturier-Doux <>
Soumis le : vendredi 18 décembre 2015 - 17:14:08
Dernière modification le : mercredi 19 septembre 2018 - 01:14:58

Identifiants

Citation

Junshi Xia, Wenzhi Liao, Jocelyn Chanussot, Peijun Du, Guanghan Song, et al.. Improving Random Forest With Ensemble of Features and Semisupervised Feature Extraction. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2015, 12 (7), pp.1471 - 1475. 〈10.1109/LGRS.2015.2409112〉. 〈hal-01246585〉

Partager

Métriques

Consultations de la notice

223