Abstract: In order to solve the problem that the interested aims are not prominent and spectral
distortion caused by different imaging mechanism of synthetic aperture radar (SAR) and visible
images, this paper proposes a fusion algorithm based on NSST-IHS and sparse representation. Firstly,
source images are transformed by intensity-hue-saturation (IHS) and non-subsampled shearlet
transform (NSST). Secondly, a fusion rule based on the structure similarity and luminance difference
of the sparse representation is used in low- frequency components, while a fusion rule based on
sum-modified-Laplacian is used in high- frequency components. Finally, the fusion results are
obtained by inverse transformation of NSST and IHS. Experiments are carried out with Sentinel-1A
SAR images and landsat-8 visible images, and compared with the traditional algorithms of IHS,
Wavelet, NSCT, IHS-Wavelet-SR and NSST-IHS. The results show that the new algorithm has
obvious improvement whether in visual or evaluation as well as to maintain the spatial structure
information and spectral information, which is beneficial to target detection and recognition.

Key words: synthetic aperture radar image, visible image, image fusion, sparse representation, non-subsampled shearlet transform