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《Computer Engineering and Applications》 2010-33
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Wavelet remote sensing image fusion based on feature product of PCA

LIU Cheng-yun1,CHEN Zhen-xue1,CHANG Fa-liang1,YIN Bing-kun2 1.School of Control Science and Engineering,Shandong University,Jinan 250061,China 2.Digital Technology Park,Science and Technology Park of Nanshan District,Shenzhen,Guangdong 518057,China  
This paper discusses the PCA(Principal Component Analysis) and a few remote sensing image fusion methods that combine the PCA and the WT(Wavelet Transform).Traditional PCA algorithms loss some useful information about spectral characteristics of the first principal component of multi-spectral images,and influence largely the spectral resolution of fusion images.A remote sensing image fusion algorithm is implemented based on the feature product of the PCA and wavelet,which makes use of the time-frequency analysis features of wavelet transform and feature product that fuses the first principal component of multi-spectral images.The improved algorithm has been tested on various multi-spectral and panchromatic satellite remote sensing images for different areas.Experiment results show that the algorithm has a better fusion result from the perspective of either subjective visual effect or objective statistical data.
【Fund】: 国家自然科学基金No.60975025;; 教育部博士点专项基金(No.20090131120039);; 中国博士后科学基金特别资助(No.200902563)~~
【CateGory Index】: TP751
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