In order to overcome the shortcomings as spectral distortion of fused images induced by only using the single energy feature to fuse image and ignoring the spectral characteristics of images coefficients in many remote sensing image fusion algorithms, a remote sensing image fusion algorithm based on second-generation curved wave transform coupled definition weighted model is proposed. Firstly, the multispectral image is computed by IHS transform, and the intensity components of the multispectral image are segmented. The high and low frequency coefficients of panchromatic image and I component are calculated by second generation curved wave transform. Then, the spectral features of the image are measured by means of the image features, and the regional energy features of the image are combined as the fusion rules of the low frequency coefficients. The definition of the image is calculated by using Laplace feature of the image, and the definition weighted model is constructed by using the calculated results to fuse the high frequency coefficients. Finally, the fusion coefficients are fused by the second generation Curvelet and IHS inverse transformation. The experimental results show that, compared with the current fusion algorithm, the proposed algorithm has higher general image quality index value and lower spectral difference index value, which indicates that the proposed algorithm has better fusion performance.