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Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

2010

Article

ei


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.

Author(s): Camps-Valls, G. and Shervashidze, N. and Borgwardt, K.
Journal: IEEE Geoscience and Remote Sensing Letters
Volume: 7
Number (issue): 4
Pages: 741-745
Year: 2010
Month: October
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1109/LGRS.2010.2046618
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web

BibTex

@article{6595,
  title = {Spatio-Spectral Remote Sensing Image Classification With Graph Kernels},
  author = {Camps-Valls, G. and Shervashidze, N. and Borgwardt, K.},
  journal = {IEEE Geoscience and Remote Sensing Letters},
  volume = {7},
  number = {4},
  pages = {741-745},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = oct,
  year = {2010},
  month_numeric = {10}
}