When applying techniques such as panoramic stitching or video fusion to outdoor environments, complex scenes and lighting conditions often lead to a decline in the algorithm’s keypoint detection capability. Curvature is a stable mathematical feature that describes image edges and exhibits good stability under complex scenes and lighting conditions. This paper delves into the extraction of multi-scale curvature features in image stitching and the Hellinger kernel transformation of the SIFT operator, proposing a multi-scale curvature feature image stitching algorithm based on Shi-Tomasi and RootSIFT. Firstly, the multi-scale Shi-Tomasi method is used to extract illumination-stable keypoints at different resolutions from Gaussian-blurred preprocessed images, making the algorithm more suitable for handling complex environments. Secondly, the RootSIFT enhanced by the Hellinger kernel transformation strengthens the multi-scale feature extraction process, making it more robust to changes in illumination and noise in Euclidean distance. Additionally, FLANN fast matching demonstrates high efficiency in processing large-scale data. Finally, in transformation estimation, the improved PROSAC algorithm of RANSAC can further enhance the speed and quality of stitching. Experimental results on detection performance show that the proposed algorithm can more accurately detect the curvature information of image edges, with feature detection capability improved by 51% compared to the original SIFT algorithm and by 182% compared to single-scale algorithms. The comparative results of multi-scale parameter groups indicate that the algorithm can achieve further optimization, comprehensively enhancing detection capability and real-time performance, demonstrating good adaptability.