China Transport Telecommunications and Information Center, Beijing 100011, China.
Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Sensors (Basel). 2020 Mar 26;20(7):1832. doi: 10.3390/s20071832.
Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas () are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra's shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency.
图像镶嵌是将多个正射影像构建成一个无缝的整体正射影像的过程，是制作大规模数字正射影像图（DOM）的关键步骤之一。缝线确定是正射影像自动镶嵌中最困难的技术之一。沿着道路中心线且没有明显差异的缝线有利于提高图像镶嵌的质量。基于此想法，本文提出了一种基于 D-LinkNet 神经网络的道路概率图的城市图像镶嵌缝线确定新方法。该方法在语义和像素级别上对缝线进行了优化，具体如下。首先，利用 D-LinkNet 神经网络获取并进行相关后处理得到道路概率图。其次，通过对左右图像重叠区域的道路概率图进行二值化，确定优先道路区域（PRAs）。PRAs 是缝线穿过的优先区域。最后，在像素级别上通过使用二进制最小堆实现的 Dijkstra 最短路径算法确定最终的缝线。三组数据集的实验结果表明了该方法的优势。与两种之前的方法相比，该方法得到的缝线穿过的不明显物体较少，主要沿着道路。在计算效率方面，该方法也具有很高的效率。
