Image fusion for synthetic aperture radar and multispectral images based on sub-band-modulated non-subsampled contourlet transform and pulse coupled neural network methods

Fusion of synthetic aperture radar (SAR) and multispectral (MS) images can contribute to a better visual perception of the objects observed. Unfortunately, many classical approaches have been proven to be unsuitable for this task due to their intrinsic differences in imaging mechanism. In the non-subsampled contourlet transform domain, an alternative fusion method based on pulse coupled neural networks is proposed. To control the amount of SAR features to be integrated into MS image, a gradient-threshold combined modulation is designed for modulating the SAR sub-band coefficients. Experiments demonstrate that the proposed method outperforms its counterparts in spectral preservation and feature enhancement.