An Upscaling–Downscaling Optimal Seamline Detection Algorithm for Very Large Remote Sensing Image Mosaicking

Abstract

For the mosaicking of multiple remote sensing images, obtaining the optimal stitching line in the overlapping region is a key step in creating a seamless mosaic image. However, for very large remote sensing images, the computation of finding seamlines involves a huge amount of image pixels. To handle this issue, we propose a stepwise strategy to obtain pixel-level optimal stitching lines for large remote sensing images via an upscaling–downscaling image sampling procedure. First, the resolution of the image is reduced and the graph cut algorithm is applied to find an energy-optimal seamline in the reduced image. Then, a stripe along the preliminary seamline is identified from the overlap area to remove the other inefficient nodes. Finally, the graph cut algorithm is applied nested within the identified stripe to seek the pixel-level optimal seamline of the original image. Compared to the existing algorithms, the proposed method produces fewer spectral differences between stitching lines and less-crossed features in the experiments. For a wide range of remote sensing images involving large data, the new method uses less than 10 percent of the time needed by the SLIC+ graph cut method.

Research on Stitching Algorithm Based on Tree Branch Image

“…Zhang, Z. Y. et al used a Gaussian pyramid to improve a simple ORB-oriented algorithm with a stitching speed about 10 times faster than SIFT [12]. Chai, X. C. et al used an up-scaling-down image sampling procedure to obtain pixel-level optimal stitching lines for large remote sensing images [13]. Kang, Y. et al proposed a novel multi-view X-ray digital imaging stitching algorithm (MVS) based on a CdZnTe photon-counting linear array detector to solve the problem of sector-beam X-ray stitching distortion [14].…”