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

Bruzzone, L; Persello, C; Chen, C.H.

Item

ITEM ACTIONSEXPORT

Add to Basket

Please note that a newer version of this item is available:
https://pure.mpg.de/pubman/item/item_1788966_2

DetailsSummary

Released

Book Chapter

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

MPS-Authors

/persons/resource/persons84133

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

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

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: https://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.