CLC number: TP7
On-line Access: 2011-06-07
Received: 2010-06-14
Revision Accepted: 2011-01-10
Crosschecked: 2011-05-24
Cited: 2
Clicked: 5955
Shan-long Lu, Le-jun Zou, Xiao-hua Shen, Wen-yuan Wu, Wei Zhang. Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations[J]. Journal of Zhejiang University Science A, 2011, 12(6): 453-460.
@article{title="Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations",
author="Shan-long Lu, Le-jun Zou, Xiao-hua Shen, Wen-yuan Wu, Wei Zhang",
journal="Journal of Zhejiang University Science A",
volume="12",
number="6",
pages="453-460",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A1000282"
}
%0 Journal Article
%T Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations
%A Shan-long Lu
%A Le-jun Zou
%A Xiao-hua Shen
%A Wen-yuan Wu
%A Wei Zhang
%J Journal of Zhejiang University SCIENCE A
%V 12
%N 6
%P 453-460
%@ 1673-565X
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1000282
TY - JOUR
T1 - Multi-spectral remote sensing image enhancement method based on PCA and IHS transformations
A1 - Shan-long Lu
A1 - Le-jun Zou
A1 - Xiao-hua Shen
A1 - Wen-yuan Wu
A1 - Wei Zhang
J0 - Journal of Zhejiang University Science A
VL - 12
IS - 6
SP - 453
EP - 460
%@ 1673-565X
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1000282
Abstract: This paper introduces a new enhancement method for multi-spectral satellite remote sensing imagery, based on principal component analysis (PCA) and intensity-hue-saturation (IHS) transformations. The PCA and the IHS transformations are used to separate the spatial information of the multi-spectral image into the first principal component and the intensity component, respectively. The enhanced image is obtained by replacing the intensity component of the IHS transformation with the first principal component of the PCA transformation, and undertaking the inverse IHS transformation. The objective of the proposed method is to make greater use of the spatial and spectral information contained in the original multi-spectral image. On the basis of the visual and statistical analysis results of the experimental study, we can conclude that the proposed method is an ideal new way for multi-spectral image quality enhancement with little color distortion. It has potential advantages in image mapping optimization, object recognition, and weak information sharpening.
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