Open AccessA new remote sensing image fusion method combining principal component analysis and curvelet transform
Author(s) -
Chao Chen,
Xinyue He,
Yanli Chu,
Xin Zhao
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/780/3/032054
Subject(s) - principal component analysis , image fusion , curvelet , artificial intelligence , pattern recognition (psychology) , fusion , inverse , image (mathematics) , computer science , computer vision , pixel , mathematics , wavelet transform , wavelet , linguistics , philosophy , geometry
In the framework of multi-scale decomposition, a new multi-source image fusion algorithm based on principal component analysis (PCA) and curvelet transform is proposed for pixel-level image fusion. Firstly the low-resolution multi-spectral image is transformed by PCA and principal components are obtained. Secondly the high-resolution image and the first three principal components of the multi-spectral image are respectively merged with curvelet transform and finally the fused image is obtained by inverse PCA transform. This method gives the results of fusion and is compared with the traditional method of PCA, which proves the effectiveness of the method.