In order to solve the problem that the spatial feature of the fusion image is not ideal because the significant content of the image is ignored in the process of information fusion, a remote sensing image fusion algorithm based on the second generation curvelet transform coupled with the significant content determination mechanism is proposed . With the help of HSV transform, the lightness component of multispectral image is calculated; then the panchromatic image and V component are calculated by the second generation curvelet transform, and the corresponding frequency-domain subband of them is output. Based on the amplitude spectrum characteristics of the image, the salient information of the image is calculated. Through the segmentation of the image, the salient content determination mechanism is established based on the salient information of the segmented image. According to the salient value of the segmented image, different methods are used to fuse the low-frequency subband. Finally, using the gradient value of the image, the detail measure factor is constructed to calculate the detail information of the image and realize the fusion of high-frequency coefficients. The experimental results show that compared with the existing remote sensing fusion scheme, the fusion image of the proposed algorithm has better spectral characteristics, showing higher standard deviation and correlation coefficient values.