Fusion of Panchromatic and Multispectral Images via Morphological Operator and Improved PCNN in Mixed Multiscale Domain

In order to effectively combine the spectral information of the multispectral (MS) image with the spatial details of the panchromatic (PAN) image and improve the fusion quality, a fusion method based on morphological operator and improved pulse coupled neural network (PCNN) in mixed multi-scale (MM) domain is proposed. Firstly, the MS and PAN images are decomposed by nonsubsampled shearlet transform (NSST) to low- and high-frequency coefficients, respectively; secondly, morphological filter-based intensity modulation (MFIM) technology and stationary wavelet transform (SWT) are applied to the fusion of the low-frequency coefficients; an improved PCNN model is employed to the fusion of the high-frequency coefficients; thirdly, the final coefficients are reconstructed with inverse NSST. The experimental results on QuickBird satellite demonstrate that the proposed method is superior to five other kinds of traditional and popular methods: HIS, PCA, SWT, NSCT-PCNN and NSST-PCNN. The proposed method can improve the spatial resolution effectively while maintaining the spectral information well. The experimental results show that the proposed method outperforms the other methods in visual effect and objective evaluations.