Hyperspectal RS image classification based on kernel methods

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.