The relevance vector machine is sparse model in the Bayesian framework, its mathematics model doesn't have
regularization coefficient and its kernel functions don't need to satisfy Mercer's condition. RVM present the good
generalization performance, and its predictions are probabilistic. In this paper, a hyperspectral imagery classification
method based on the relevance machine is brought forward. We introduce the sparse Bayesian classification model,
regard the RVM learning as the maximization of marginal likelihood, and select the fast sequential sparse Bayesian
learning algorithm. Through the experiment of PHI imagery classification, the advantages of the relevance machine used
in hyperspectral imagery classification are given out.
Hyperspectral RS technology organically combines the radiation information and the space information. The spectrum
information, which the hyperspectral image enriches, can be better to carry on the ground target classification, compare
with panchromatic remote sensing image and multispectral remote sensing image. As support vector machine was
applied to many fields successfully recent years, using kernel methods, the classic linear methods can cope with the
nonlinear problem, which was called the 3rd revolution of pattern analysis algorithms. This paper introduced two
classifying methods for hyperspectral image based on kernel function, Support Vector Machine and Kernel Fisher
Discriminant Analysis, and studied the selection of kernel function and its parameters as well as multi-class
decomposition. We use radial basic function kernel, one against one or one against rest decomposition methods to
construct multi-class classifier, and optimize parameter selection using cross-validating grid search to build an effective
and robust kernel classifier. It is verified that, through the OMIS and AVIRIS image classifying experiments, comparing
with common image classifying methods, kernel classifying method can avoid Hughes phenomenon, thus improve the
classifying accuracy.
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