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): | Empirische Inferenz |
Bibtex Type: | Article (article) |
Digital: | 0 |
DOI: | 10.1109/LGRS.2010.2046618 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
Web
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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}, doi = {10.1109/LGRS.2010.2046618}, month_numeric = {10} } |