NDLI: A Fusion Algorithm of Remote Sensing Images Based on Normalized Correlation Moment of Characteristic Values

Content Provider	IEEE Xplore Digital Library
Author	Linling Zhang Lizhong Xu Fengchen Huang
Copyright Year	2007
Description	Author affiliation: Univ. Nanjing, Nanjing (Linling Zhang)
Abstract	Now multi-spectral images and panchromatic images are widely used in remote sensing. In this paper we proposed an algorithm using normalized correlation moment of coefficient characteristic values based on principal component analysis and wavelet transform. The basic idea of this algorithm is to transform the multi-spectral image into three principal components at first, then perform wavelet decomposition of the first component image and the panchromatic image separately. Using normalized correlation moment fusion formula to select the wavelet coefficient of high frequency component in different resolutions. Finally, by performing inverse PCA transform to get the fused image. We compared the fused image using this algorithm with the results of PCA, DWT and other familiar methods, computer simulation shows that the fused image using this algorithm obtain better results in terms of both preserving spectral information and improving spatial resolution of the original multi-spectral images than the others.
Starting Page	465
Ending Page	469
File Size	3578949
Page Count	5
File Format	PDF
ISBN	1424412196
DOI	10.1109/ICIA.2007.4295778
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-08
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet transforms Multispectral imaging Computer simulation Normalized Correlation Moment Wavelet analysis Remote Sensing Discrete wavelet transforms Remote sensing Wavelet coefficients Wavelet Transform Image Fusion Frequency PCA Transform Spatial resolution Principal component analysis
Content Type	Text
Resource Type	Article

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