Multi-Feature Kernel Based Classification for Hyperspectral Remote Sens-ing Image
Kernel function is an important clement which can absolutely affect the ability of Support Vector Machine(SVM),most of kernel functions are made of spectral distance only today,and they of-ten ignore the spectral angle which is an important feature of an image.This paper proposes a SVM classifier who's kernel is made of multi-feature Equalized Spectral Angle and Distance(ESAD),and classes Pavia University,Italy with ROSIS hyperspectral data which was gotten in 2003 with that.It al-so evaluates the accuracy of the classified image.It turns that the ESAD kernel SVM gets the overall accuracy increased by 8.88％and 11.03％comparing with spectral angle kernel SVM and spectral distance kernel SVM,the accuracy is optimistic and the method can solve the problem of"different ob-jects with same spectral curve"during image classifying.
Hyperspectral Remote SensingSupport Vector Machinespectral angleclassifica-tionkernel function
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