K-l transform based image fusion of C++ code
2016-08-23
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KL transform L2-PCA image fusion multi-spectral image panchromatic image
L2-pca use of multi-spectral image and panchromatic image image integration, for the principal component analysis of the characteristics, will be applied to image fusion, multi-band image can maximize the performance of the information is first calculated in the fusion of the new image participated in the two source image fusion covariance matrix, and then find the corresponding eigenvalues eigenvectors. Finally, with a maximum value corresponding to the characteristic feature vectors to determine the weighting factor of two images.
L2-pca use of multi-spectral image and panchromatic image image integration, for the principal component analysis of the characteristics, will be applied to image fusion, multi-band image can maximize the performance of the information is first calculated in the fusion of the new image participated in the two source image fusion covariance matrix, and then find the corresponding eigenvalues eigenvectors. Finally, with a maximum value corresponding to the characteristic feature vectors to determine the weighting factor of two images.
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