A hyperspectral image fusion method based on second generation wavelet with variance weighting is proposed in this
paper. This method includes three major steps: Firstly, decompose the original 220 bands image by second generation
wavelet transform, namely predict and update sub-images on rectangle and quincunx grids by Neville filters. Secondly,
use variance as fusion weight to multiply decomposed coefficients. Finally the fused image was reconstructed by reverse
second generation wavelet transform. AVIRIS hyperspectral image was selected in the experiments, the results of which
illustrated that the method based on second generation wavelet can utilize both spatial and spectral characteristics of
source images more adequately. This novel method improved qualitative and quantitative results, compared to previous
wavelet fusion methods. Therefore, the effect of variance weighting fusion is superior to that of averaging fusion.