To solve the current issues of low effective matching rate and large stitching error of the remote sensing image mosaic algorithm, the matching and fusion/stitching processes of a remote sensing image mosaic algorithm are investigated herein. This algorithm utilizes a guided filter to preprocess the images. Based on the fast and efficient speed-up robust feature approach and the low-error bidirectional mutual selection matching strategy, the matching feature points are further purified by the random sample consensus algorithm; then, a homography transformation matrix is adopted to calculate the relative positional relationship between the images. In the fusion/stitching phase, the image fusion algorithm is modified according to Weber's law and the plant growth function. Finally, image fusion stitching is completed using the nonlinear S-type nonlinear fusion strategy. The simulations reveal improvements in the matching accuracy, average gradient of the image fusion/stitching resultant graph, and information entropy by approximately 1.01%-3.42%, 84.86%-146.26%, and 0.77%-2.22%, respectively. Thus, with respect to realizing high-efficiency and low-error remote sensing image matching, improved splicing quality and efficiency as well as high robustness of the fusion algorithm are achieved.