  Kernel-based machine learning algorithms are based on mapping data from the
original input feature space to a kernel feature space of higher dimensionality
to solve a linear problem in that space. Over the last decade, kernel based
classification and regression approaches such as support vector machines have
widely been used in remote sensing as well as in various civil engineering
applications. In spite of their better performance with different datasets,
support vector machines still suffer from shortcomings such as
visualization/interpretation of model, choice of kernel and kernel specific
parameter as well as the regularization parameter. Relevance vector machines
are another kernel based approach being explored for classification and
regression with in last few years. The advantages of the relevance vector
machines over the support vector machines is the availability of probabilistic
predictions, using arbitrary kernel functions and not requiring setting of the
regularization parameter. This paper presents a state-of-the-art review of SVM
and RVM in remote sensing and provides some details of their use in other civil
engineering application also.
