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Tuia, Devis, Ratle, Frederic, Pozdnoukhov, Alexei and Camps-Valls, Gustavo (2010) Multisource Composite Kernels for Urban-Image Classification. Geoscience and Remote Sensing Letters, IEEE , 7 (1). pp. 88-92. ISSN 1545-598X
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Abstract
This letter presents advanced classification methods
for very high resolution images. Efficient multisource information,
both spectral and spatial, is exploited through the use of composite
kernels in support vector machines. Weighted summations of
kernels accounting for separate sources of spectral and spatial
information are analyzed and compared to classical approaches
such as pure spectral classification or stacked approaches using
all the features in a single vector. Model selection problems are
addressed, as well as the importance of the different kernels in the
weighted summation.
| Item Type: | Article |
|---|---|
| Keywords: | Multiple kernel learning; support vector machines (SVMs); urban monitoring; very high resolution image; |
| Academic Unit: | Faculty of Science and Engineering > Research Institutes > National Centre for Geocomputation, NCG |
| Item ID: | 2138 |
| Depositing User: | Dr Alexei Pozdnoukhov |
| Date Deposited: | 29 Sep 2010 15:26 |
| Journal or Publication Title: | Geoscience and Remote Sensing Letters, IEEE |
| Publisher: | IEEE |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/2138 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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