The hyperspectral remote sensing technology

which appeared early in 1980s

combines the radiation information which relates to the targets’ attribute

and the space information which relates to the targets’ position and shape

completing the information continuum of optics RS image from panchromatic image to hyperspectral via multi-spectral image.The spectrum information

which is rich in the hyperspectral image

compared with panchromatic remote sensing image and multispectral remote sensing image

can be used to classify the ground target better.It has become an important technique of map cartography

vegetation investigation

ocean remote sensing

agriculture remote sensing

atmosphere research

environment monitoring and military information acquiring.As Support Vector Machine（SVM） was applied to machine learning fields successfully in recent years

the classic linear pattern analysis algorithms which was called the 3rd revolution of pattern analysis algorithms

can cope with the nonlinear problem.Some references applied the kernel methods to linear Fisher Discriminant Analysis（FDA）

and put forward Kernel Fisher Discriminant Analysis（KFDA）.Firstly

this paper introduced the classification method based on the kernel fisher discriminant analysis.For the binary problem

the aim of FDA is to find out the linear projection（projection axes） on which the intra-class scatter matrices of the training samples are maximized and scatter matrices of inter-class are minimized.For KFDA

the inputted data is mapped into a high dimensional feature space by a nonlinear mapping

while linear FDA in the feature space will be performed.Secondly

we researched on the selection methods of the kernel function and its parameter

and studied on the multi-classes classification methods

and then applied them to hyperspectral remote sensing classification.We use decomposition methods of multi-class classifier and method of parameter selection using cross-validating grid search to build an effective and robust KFDA classifier.Finally

we carried out the hyperspectral image classification experiments based on KFDA and some other comparative experiments.Some conclusions can be drew as follows.Using the kernel mapping

the KFDA experiment on PHI and AVIRIS image demonstrates that the KFDA is less affected by the dimension of input sample

and can avoid the Hughes phenomena effectively.The results show that it has more comparable classification accuracy than support vector machine classifier.There is no need to compute the complicated quadratic optimizing problem in training KFDA classifier as SVM classifier does

so this algorithm is not very complicated and costs less time.Especially in the one-against-rest decomposition

comparing with the SVM

KFDA is much faster.The capability of KFDA classifier is affected a lot by kernel function and its parameters

and a fine recognition precision can only be obtained when the kernel function’s parameters are appropriate.The stability of classification can be effectively improved by parameter selection via cross-validate grid search method.