Multisource Composite Kernels for Urban-Image Classification

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...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2010-01, Vol.7 (1), p.88-92
Main Authors: Tuia, D., Ratle, F., Pozdnoukhov, A., Camps-Valls, G.
Format: Article
Language:English
Subjects:
Accounting
Classification
Image resolution
Information analysis
Kernel
Kernels
Machine learning
Mathematical analysis
Multiple kernel learning
Optical filters
Risk analysis
Satellites
Spatial resolution
Spectra
Support vector machine classification
Support vector machines
support vector machines (SVMs)
urban monitoring
Vectors (mathematics)
very high resolution image
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Summary: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.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2009.2015341