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Book Chapter

Approaches Based on Support Vector Machine to Classification of Remote Sensing Data


Persello,  C
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bruzzone, L., & Persello, C. (2010). Approaches Based on Support Vector Machine to Classification of Remote Sensing Data. In Handbook of Pattern Recognition and Computer Vision (pp. 329-352). London, UK: ICP.

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This chapter presents an extensive and critical review on the use of kernel methods and in particular of support vector machines (SVMs) in the classification of remote-sensing (RS) data. The chapter recalls the mathematical formulation and the main theoretical concepts related to SVMs, and discusses the motivations at the basis of the use of SVMs in remote sensing. A review on the main applications of SVMs in classification of remote sensing is given, presenting a literature survey on the use of SVMs for the analysis of different kinds of RS images. In addition, the most recent methodological developments related to SVM-based classification techniques in RS are illustrated by focusing on semisupervised, domain adaptation, and context sensitive approaches. Finally, the most promising research directions on SVM in RS are identified and discussed.