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.

[1]  Patrick Hostert,et al.  imageSVM-A USER-ORIENTED TOOL FOR ADVANCED CLASSIFICATION OF HYPERSPECTRAL DATA USING SUPPORT VECTOR MACHINES , 2007 .

[2]  Ji Zhu,et al.  Kernel Logistic Regression and the Import Vector Machine , 2001, NIPS.

[3]  Pramod K. Varshney,et al.  Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data , 2006, IEEE Geoscience and Remote Sensing Letters.

[4]  Gavin C. Cawley,et al.  Efficient model selection for kernel logistic regression , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[5]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[6]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Jon Atli Benediktsson,et al.  Fusion of Support Vector Machines for Classification of Multisensor Data , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Other.  Directive 2000/60/EC of the European Parliament and of The Council of 23 October 2000 establishing a Framework for Community Action in the Field of Water Policy (Water Framework Directive) , 2000 .

[10]  Giles M. Foody,et al.  RVM‐based multi‐class classification of remotely sensed data , 2008 .

[11]  Ribana Roscher,et al.  Kernel Discriminative Random Fields for land cover classification , 2010, 2010 IAPR Workshop on Pattern Recognition in Remote Sensing.

[12]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[13]  S. Sathiya Keerthi,et al.  A Fast Dual Algorithm for Kernel Logistic Regression , 2002, 2007 International Joint Conference on Neural Networks.