Fast And Efficient Method For Large-Scale Aerial Image Stitching

Recent studies on image stitching have been extensively conducted to stitch panoramic or 360 degrees images using a small number of input images. The stitching of aerial images, that are captured by unmanned aerial vehicles (UAVs), has various practical applications. In this paper, we propose a fast adaptive stitching algorithm for handling numerous aerial images. First, the proposed method analyzes the relative positions and overlapping regions of the UAV image footprints by exploiting their geotag information. Based on the analysis, an adaptive selection algorithm is proposed to eliminate the densely overlapped images from among all the UAV images. Then, the proposed method sequentially performs fast feature extraction and feature matching. Finally, a local warp method, with a smooth transition for overlapping regions, is introduced to alleviate the blurring artifacts and achieve highly accurate image alignment. The experiments are conducted for various scenarios to generate seamless terrestrial mosaic images of large areas. The proposed method improves the visual quality of the stitched image, by decreasing the estimated reprojection error and the number of observed visual distortions. In addition, the proposed method can substantially reduce the processing time compared with conventional stitching methods.