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《Acta Photonica Sinica》 2009-12
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A Novel Spectral Similarity Measurement Kernel Based Anomaly Detection Method in Hyperspectral Imagery

MEI Feng,ZHAO Chun-hui,SUN Yan,WANG Li-guo(College of Information & Communication Engineering,Harbin Engineering University,Harbin 150001,China)  
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.
【Fund】: 国家自然科学基金(60802059);; 高等学校博士学科点基金(20060217021);; 黑龙江省自然科学重点基金(ZJG0606-01)资助
【CateGory Index】: TP751.1
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