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.