de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Disclaimer Contact us Login
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Book Chapter

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

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84133

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

Locator
There are no locators available
Fulltext (public)
There are no public fulltexts available
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C19C-3
Abstract
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.