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SAR images recognition based on kernel principal component analysis

Yu Hongyun 1,2,Jiang Tao3,Guan Jian 1 1.Research Institute of Information Fusion,Naval Aeronautical Engineering Institute,Yantai 264001,China; 2.School of Mathematics and Information Ludong University,Yantai 264025,China; 3.Department of Ordnance Science and Technology Naval Aeronautical Engineering Institute,Yantai 264001,China  
A kernel principle component analysis method based on tensor algebra is proposed for feature extraction.It can reduce the huge computation cost due to increasing dimensions,while considering the information of known classes.First the kernel principle component analysis method is applied to each class of targets to build their corresponding feature spaces.Then,the collection of feature spaces is unified into a higher dimensional space after introducing the operation of the tensor product.Hence,a linear principle component analysis method can be directly applied on this feature space in order to construct the proper feature space to both reflect the characters of each class and lower the cost of computation.The recognition experiments showed that the cost of computation and memory can be decreased heavily compared to the approach that builds the feature space by using the kernel principle component analysis method directly.
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