An Improved APAP Algorithm via Line Segment Correction for UAV Multispectral Image Stitching

Unmanned aerial vehicles (UAV) low-altitude remote sensing has been extensively applied in agriculture as an important way of monitoring the growth and physiological conditions of crops. Due to the limited imaging width, multispectral images captured by UAV need to be stitched in order to obtain the panoramic image of the whole area. However, sometimes it is difficult to stitch multispectral images by using the traditional stitching methods when there is blurring or uneven local brightness of the images. Accordingly, in this paper, we propose an effective and robust UAV multispectral image stitching method based on an improved As-Projective-As-Possible (APAP) algorithm via line segment correction. We handle the stitching problem in two steps: firstly, inter-band registration and global homography transformation are used to ensure the precision of stitching results. Then the line segment detection and correction based on line segment detector and least-square estimation is conducted, which can protect the unique line segment structure in farmland area. The experimental results substantiate the superiority of our method both visually and quantitatively when compared with state-of-the-art methods.