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《Science of Surveying and Mapping》 2015-06
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Classification of hyper-spectral imagery based on 2DPCA

YANG Ming;ZHANG Peng-qiang;YU Xu-chu;LIU Zhong-bin;CHEN Xue-shui;Troops 61175;Institute of Surveying and Mapping,Information Engineering University;  
Traditional feature extraction method are based on vector model,which not only leads to high dimensions,but also loses pixel's space information.In the paper,a feature extraction based on two dimensions principal component analysis was proposed to solve the problems.This method could make full use of data's space information,it could also find the best project's direction by linear transformation.At the same time,it could elimate the final results of the"pitting"phenomena and avoid classification surface features confusion.The experiment result showed that the the proposed method was effective.
【Fund】: 国家自然科学基金项目(41201477)
【CateGory Index】: P237
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