In order to solve the problem of blocking effect and blurring effect induced by ignoring the edge features of image region pixels t in current remote sensing image fusion algorithms. the remote sensing image fusion algorithm based on edge constraint model and nonsubsampled shearlet transform was proposed in this paper. Firstly, multispectral images are decomposed by luminance hue saturation decomposition to extract luminance components. Then, the panchromatic image and luminance components are decomposed by nonsubsampled Shearlet transform to obtain highfrequency coefficients and lowfrequency coefficients. Finally, the fusion function of lowfrequency coefficients is established through the spatial frequency characteristics of the image to fuse the lowfrequency coefficients. An edge constraint model is constructed to fuse the high frequency coefficients by using the average gradient feature and the edge energy feature of pixels in the image region. After the fusion, the lowfrequency coefficients and highfrequency coefficients are inversely transformed by nondownsampling Shearlet, then the fused images are obtained by inverse transform of IHS. The experimental results show that, compared with the current remote sensing image fusion methods, the fusion images designed in this paper not only have better clarity, but also have better spectral characteristics without blocking effect and blurring effect.