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  Spatio-Spectral Remote Sensing Image Classification With Graph Kernels

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BDD4-D Version Permalink: http://hdl.handle.net/21.11116/0000-0002-6A99-B
Genre: Journal Article

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Camps-Valls, G, Author              
Shervashidze, N1, 2, Author              
Borgwardt, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: 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.

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 Dates: 2010-10
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1109/LGRS.2010.2046618
BibTex Citekey: 6595
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Title: IEEE Geoscience and Remote Sensing Letters
  Other : IEEE Geosci. Remote Sens. Lett.
Source Genre: Journal
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Publ. Info: Piscataway, NJ : Institute of Electrical and Electronics Engineers
Pages: - Volume / Issue: 7 (4) Sequence Number: - Start / End Page: 741 - 745 Identifier: ISSN: 1545-598X
CoNE: https://pure.mpg.de/cone/journals/resource/954925491886