Multi-source composite kernels for urban image classification

Details

Serval ID
serval:BIB_E77FEFC4A6DC
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Multi-source composite kernels for urban image classification
Journal
IEEE Geoscience and Remote Sensing Letters
Author(s)
Tuia D., Ratle F., Pozdnoukhov A., Camps-Valls G.
ISSN-L
1545-598X
Publication state
Published
Issued date
01/2010
Peer-reviewed
Oui
Volume
7
Pages
88-92
Language
english
Notes
Tuia2010e
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.
Keywords
Multiple kernel learning, support vector machines (SVMs), urban monitoring, , very high resolution image
Create date
25/02/2009 9:52
Last modification date
20/08/2019 16:10
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