Unmanned aerial vehicle aerial image stitching method based on superpixel segmentation

Abstract Addressing the issues of long processing time, high computational complexity, and poor stitching quality in existing methods for unmanned aerial vehicle (UAV) aerial image stitching, this paper proposes an aerial image stitching method based on similar region estimation. Firstly, the input set of UAV aerial images is subjected to superpixel segmentation to estimate similar regions among the images. Secondly, an improved SIFT algorithm is used to extract and match feature points within the similar regions across the image set. Finally, based on the obtained matching points, an improved optimal seam-line algorithm and a weighted average fusion algorithm are employed to fuse the images, resulting in the stitched aerial image. Experimental validation using the public dataset UAV-image-mosaicing dataset demonstrates that our method achieves nearly double the speed of feature extraction compared to traditional feature extraction algorithms and triples the feature matching rate. Furthermore, compared to mainstream UAV aerial image stitching methods, our approach reduces time consumption by half while improving the stitching image evaluation metrics of SSIM, MAE, and PSNR by approximately 5% on average.