This paper presents a semisupervised support vector machine (SVM)
that integrates the information of both labeled and unlabeled pixels
efficiently. Method's performance is illustrated in the relevant
problem of very high resolution image classification of urban areas.
The SVM is trained with the linear combination of two kernels: a
base kernel working only with labeled examples is deformed by a likelihood
kernel encoding similarities between labeled and unlabeled examples.
Results obtained on very high resolution (VHR) multispectral and
hyperspectral images show the relevance of the method in the context
of urban image classification. Also, its simplicity and the few parameters
involved make the method versatile and workable by unexperienced
users.