Unmanned aerial vehicle image stitching based on multi-region segmentation

Unmanned aerial vehicle (UAV) image stitching is a key technology for aerial remote sensing applications. Most existing image stitching methods based on optimal seamline searching algorithms can eliminate defects such as ghosting and distortion in stitched images, which unfortunately suffer from the problem that the seamline may cross those regions with significant geometric misalignment between different images. Therefore, a novel image stitching method based on multi-region image segmentation is proposed. The algorithm starts by performing a multi-scale morphological reconstruction in the overlapping regions between UAV images to obtain superpixel images with precise contours. Then, the fast density peaks clustering based on K-nearest neighbours is applied to automatically determine the clustering centres and the number of clusters. By constructing a cost function, an energy map is generated in the overlapping regions between UAV images. Finally, the optimal seamline can be determined with a graph-cut method. Compared to several popular image stitching algorithms in real experiments, the proposed method can essentially prevent the seamline from crossing significant ground objects to ensure the integrity of structural objects while achieving satisfactory accuracy and efficiency during the UAV image stitching process.