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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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Camps-Valls, G1, Author           
Shervashidze, N2, Author           
Borgwardt, K2, Author           
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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: Issued
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Title: IEEE Geoscience and Remote Sensing Letters
Source Genre: Journal
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Pages: - Volume / Issue: 7 (4) Sequence Number: - Start / End Page: 741 - 745 Identifier: -