Abstract：Aiming at the problem that the spatial details and spectral features of PCA-Brovey algorithm could not be improved
simultaneously in fusing multi-spectral remote sensing images (MS) and panchromatic images (PAN), a remote sensing image fusion method
based on KPCA-Brovey was proposed. Firstly, by utilizing the characteristics of non-linear spectral data mining based on Kernel
Principal Component Transform (KPCA) algorithm, three principal components including KPC1, KPC2 and KPC3 with the largest amount of
information in multispectral images were extracted. Then, three principal components and panchromatic bands were normalized by Brovey
algorithm to enrich the spatial and spectral information in the fusion results. Finally, the experimental results with two satellites
data, from Landsat5-TM (Wuhan District) and QuickBird (Foshan District), indicated that the proposed method obviously outperformed the
PCA-Brovey method in spectral and spatial information extracting. The spectral information extracting rates were increased to 96.6 %
(Wuhan District) and 96.1% (Foshan District) respectively. The spectral correlation coefficients were increased to 0.9079（Wuhan
District）and 0.8979(Foshan District) respectively.
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2019, Vol. 38 
