To improve the fusion performance of the domestic synthetic aperture radar (SAR) and optical images, multi-spectral images of GF-1 and GF-2 and the SAR spotlight (SL) and fine stripe 1 (FS1) model images of GF-3 are used. Using two different fusion ideas of transform domain and space domain, we propose a fusion algorithm, namely IHS_NSST, based on non-subsampled shearlet wave transform (NSST) combined with the intensity-hue-saturation (IHS) transform and the regional improved pulse coupled neural network (PCNN) method. In this algorithm, IHS transform is firstly performed on multi-spectral images. Secondly, in the sub-band of NSST decomposition, the regional idea is used. The region energy averaging algorithm is used for low frequency components, and the PCNN algorithm based on the improved sum of modified Laplacian (SML) excitation is used for high frequency components. Finally, the proposed algorithm is compared with many fusion methods in terms of qualitative and quantitative evaluations. The results show that the fusion method based on regional IHS_NSST has a great advantage in the fusion of the high resolution SAR and optical images. The proposed method greatly improves the fusion performance, reduces spectral distortion, maintains spatial feature information better, and improves the availability of domestic high resolution SAR and optical images.