A novel spectral similarity measurement kernel function is proposed and applied to anomaly detection in hyperspectral imagery.As the Gaussian Radial Basis Function (RBF) is based on the Euclidean distance of two spectral vectors,it is sensitive for distance variations of two spectral vectors,but not for spectral curve variation coming from radiation intensity variation,shadow,and shading etc.When the spectral curves of a material are variety,the detection performance of the RBF based anomaly detectors degenerate.In order to solve the spectral curves variation problems for the same materials,a spectral similarity measurement kernel function is proposed according to the spectral curves similarity description.A theoretical analysis is expounded and numerical experiments are conducted on real hyperspectral imagery.The detection result comparison of Gaussian Radial Basis Function based and Spectral Similarity Measurement Kernel based anomaly detector shows the Spectral Similarity Measurement kernel can improve the performance of kernel base anomaly detection methods in hyperspectral imagery.