A Remote Sensing Image Fusion Algorithm Based on Nonnegative Ordinal Independent Component Analysis by Using Lagrange Algorithm

2008 ◽  
Author(s):  
Zhongni Wang ◽  
Xianchuan Yu ◽  
Wang Yu ◽  
Dai Sha
Keyword(s):  
Remote Sensing ◽  
Independent Component Analysis ◽  
Image Fusion ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Independent Component ◽  
Fusion Algorithm ◽  
Remote Sensing Image Fusion

Remote sensing image fusion via RPCA and adaptive PCNN in NSST domain

2018 ◽  
Vol 16 (05) ◽  
pp. 1850037 ◽  
Author(s):  
Kang Zhang ◽  
Yongdong Huang ◽  
Cheng Zhao
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Image Fusion ◽  
High Frequency ◽  
Principal Component ◽  
Fusion Rule ◽  
Remote Sensing Image ◽  
Component Analysis ◽  
Fusion Algorithm ◽  
Remote Sensing Image Fusion

In order to improve fused image quality of multi-spectral (MS) image and panchromatic (PAN) image, a new remote sensing image fusion algorithm based on robust principal component analysis (RPCA) and non-subsampled shearlet transform (NSST) is proposed. First, the first principle component PC1 of MS image is extracted via principal component analysis (PCA). Then, the component PC1 and PAN image are decomposed by NSST to get the low and high frequency subbands, respectively. For the low frequency subband, the sparse matrix of PAN image by RPCA decomposition is used to guide the fusion rule; for the high frequency subbands, the fusion rule employed is based on adaptive PCNN model. Finally, the fusion image is obtained by inverse NSST transform and inverse PCA transform. The experimental results illustrate that the proposed fusion algorithm outperforms other classical fusion algorithms (PCA, Curvelet, NSCT, NSST and NSCT-PCNN) in terms of visual quality and objective evaluation in whole, and achieve better fusion performance.

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