IMPORT VECTOR MACHINES BASED CLASSIFICATION OF MULTISENSOR REMOTE SENSING DATA

The classification of multisensor data sets, consisting of multitemporal SAR data and multispectral is addressed. In the present study, Import Vector Machines (IVM) are applied on two data sets, consisting of (i) Envisat ASAR/ERS-2 SAR data and a Landsat 5 TM scene, and (ii) TerraSAR-X data and a RapidEye scene. The performance of IVM for classifying multisensor data is evaluated and the method is compared to Support Vector Machines (SVM) in terms of accuracy and complexity. In general, the experimental results demonstrate that the classification accuracy is improved by the multisensor data set. Moreover, IVM and SVM perform similar in terms of the classification accuracy. However, the number of import vectors is considerably less than the number of support vectors, and thus the computation time of the IVM classification is lower. IVM can directly be applied to the multi-class problems and provide probabilistic outputs. Overall IVM constitutes a feasible method and alternative to SVM.