Tuia, Devis and Ratle, Frederic and 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
URI:	http://ieeexplore.ieee.org/Xplore/dynhom...
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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