de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83843

Camps-Valls,  G
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84919

Shervashidze,  N
Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons75313

Borgwardt,  K
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Camps-Valls, G., Shervashidze, N., & Borgwardt, K. (2010). Spatio-Spectral Remote Sensing Image Classification With Graph Kernels. IEEE Geoscience and Remote Sensing Letters, 7(4), 741-745. doi:10.1109/LGRS.2010.2046618.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-BDD4-D
Zusammenfassung
This letter presents a graph kernel for spatio-spectral remote sensing image classification with support vector machines (SVMs). The method considers higher order relations in the neighborhood (beyond pairwise spatial relations) to iteratively compute a kernel matrix for SVM learning. The proposed kernel is easy to compute and constitutes a powerful alternative to existing approaches. The capabilities of the method are illustrated in several multi- and hyperspectral remote sensing images acquired over both urban and agricultural areas.