When using simple linear iterative clustering (SLIC) algorithm for super-pixel segmentation of remote sensing images, there are problems of long running time and poor edge fitting. Therefore, a super-pixel segmentation algorithm of remote sensing image based on improved SLIC is proposed in this paper. First, the initialization method of initial seed points is improved to eliminate the influence of random distribution. Second, after each iteration, a filtering operation is introduced to remove pixels in the super-pixel that are significantly different from the clustering center in color space, and the clustering center is updated with the remaining pixel points. Finally, the super-pixel segmentation is realized by iteration with the improved mean value calculation formula. The experimental results in the Python environment show that in the case of the same number of super pixels, compared with classic SLIC algorithm, this algorithm reduces the segmentation error rate by 7.4%, improves the segmentation accuracy by 1.4%. It can effectively improve the fit of the edge contour and reduce the computational complexity of the algorithm.