Abstract
The classification of remote sensing data with imbalanced training data is addressed. The classification accuracy of a supervised method is affected by several factors, such as the classifier algorithm, the input data and the available training data. The use of an imbalanced training set, i.e., the number of training samples from one class is much smaller than from other classes, often results in low classification accuracies for the small classes. In the present study support vector machines (SVM) are trained with imbalanced training data. To handle the imbalanced training data, the training data are resampled (i.e., bagging) and a multiple classifier system, with SVM as base classifier, is generated. In addition to the classifier ensemble a single SVM is applied to the data, using the original balanced and the imbalanced training data sets. The results underline that the SVM classification is affected by imbalanced data sets, resulting in dominant lower classification accuracies for classes with fewer training data. Moreover the detailed accuracy assessment demonstrates that the proposed approach significantly improves the class accuracies achieved by a single SVM, which is trained on the whole imbalanced training data set.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Huang, C., Davis, L.S., Townshend, J.R.: An assessment of support vector machines for land cover classification. Int. J. Remote Sens. 23, 725–749 (2002)
Foody, G.M., Mathur, A.: A Relative Evaluation of Multiclass Image Classification of Support Vector Machines. IEEE Trans. Geosci. and Remote Sens. 42, 1335–1343 (2004)
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. and Remote Sens. 42, 1778–1790 (2004)
Polikar, R.: Ensemble Based Systems in Decision Making. IEEE Circuits and Systems Magazine 6, 21–45 (2006)
Benediktsson, J.A., Chanussot, J., Fauvel, M.: Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments. In: Haindl, M., Kittler, J., Roli, F. (eds.) MCS 2007. LNCS, vol. 4472, pp. 501–512. Springer, Heidelberg (2007)
Benediktsson, J.A., Kanellopoulos, I.: Classification of Multisource and Hyperspectral Data Based on Decision Fusion. IEEE Trans. Geosci. and Remote Sens. 37, 1367–1377 (1999)
Briem, G.J., Benediktsson, J.A., Sveinsson, J.R.: Multiple Classifiers Applied to Multisource Remote Sensing Data. IEEE Trans. Geosci. Remote Sens. 40, 2291–2299 (2002)
Waske, B., Benediktsson, J.A.: Fusion of Support Vector Machines for Classification of Multisensor Data. IEEE Trans. Geosci. and Remote Sens. 45, 3858–3866 (2007)
Waske, B., van der Linden, S.: Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Trans. on Geosci. and Remote Sens. 46, 1457–1466 (2008)
Breiman, L.: Bagging predictors. Mach. Learning 24, 123–140 (1996)
Kim, H.-C., Pang, S., Je, H.-M., Kim, D., Bang, S.Y.: Constructing support vector machine ensemble. Pattern Recogn. 36, 2757–2767 (2003)
Zortea, M., De Martino, M., Serpico, S.: A SVM ensemble approach for spectral-contextual classification of optical high spatial resolution imagery. In: Proc. of IGARSS 2007 Symposium, Barcelona, Spain (2007)
Trebar, M., Steele, N.: Application of distributed SVM architectures in classifying forest data cover types. Comp. and Electr. in Agriculture 63, 119–130 (2008)
Imam, T., Ting, K.M., Kamruzzaman, J.: z-SVM: An SVM for Improved Classification of Imbalanced Data. In: Sattar, A., Kang, B.-h. (eds.) AI 2006. LNCS, vol. 4304, pp. 264–273. Springer, Heidelberg (2006)
Kang, P., Cho, S.: EUS SVMs: Ensemble of Under-Sampled SVMs for Data Imbalance Problems. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4232, pp. 837–846. Springer, Heidelberg (2006)
Barandela, R., Valdovinos, R., Sánchez, J.: New Applications of Ensembles of Classifiers. Pattern Analy. & Appl. 6, 245–256 (2003)
Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (1998)
Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Waske, B., Benediktsson, J.A., Sveinsson, J.R. (2009). Classifying Remote Sensing Data with Support Vector Machines and Imbalanced Training Data. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-642-02326-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02325-5
Online ISBN: 978-3-642-02326-2
eBook Packages: Computer ScienceComputer Science (R0)