Ensemble Strategies for Classifying Hyperspectral Remote Sensing Data

The classification of hyperspectral imagery, using multiple classifier systems is discussed and an SVM-based ensemble is introduced. The data set is separated into separate feature subsets using the correlation between the different spectral bands as a criterion. Afterwards, each source is classified separately by an SVM classifier. Finally, the different outputs are used as inputs for final decision fusion that is based on an additional SVM classifier. The results using the proposed strategy are compared to classification results achieved by a single SVM and other well known classifier ensembles, such as random forests, boosting and bagging.

• Type: Conference
• Year: 2009
• Where: MCS
• Authors: Xavier Ceamanos, Björn Waske, Jon Atli Benediktsson, Jocelyn Chanussot, Johannes R. Sveinsson
• Keywords: Additional Svm Classifier, MCS 2009, Pattern Recognition, Separate Feature Subsets, SVM Classifier