In this paper, we present and apply a semisupervised support vector
machine based on cluster kernels for the problem of very high resolution
image classification. In the proposed setting, a base kernel working
with labeled samples only is deformed by a likelihood kernel encoding
similarities between unlabeled examples. The resulting kernel is
used to train a standard support vector machine (SVM) classifier.
Experiments carried out on very high resolution (VHR) multispectral
and hyperspectral images using very few labeled examples show the
relevancy of the method in the context of urban image classification.
Its simplicity and the small number of parameters involved make it
versatile and workable by unexperimented users.