Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network
Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network
复制标题
基于 D-LinkNet 神经网络道路概率图的城市图像马赛克自动接缝线确定
DOI：
10.3390/s20071832
复制
发表时间：
2020-04-01
期刊：
影响因子：
3.9
通讯作者：
Cai, Hongyue
中科院分区：
文献类型：
--
作者：
Yuan, Shenggu;Yang, Ke;Cai, Hongyue
影像拼接是将多幅正射影像拼接成一幅无缝合成的正射影像的过程，是生产大比例尺数字正射影像图的关键步骤之一。在正射影像自动拼接中，拼接线的确定是最困难的技术之一。沿道路中心线的接缝线在没有显著差异的情况下有利于提高图像拼接的质量。基于这一思想，本文提出了一种基于D-LinkNet神经网络道路概率图的城市图像拼接接缝线确定方法。此方法在语义和像素级别优化接缝线，如下所示。首先，利用D-LinkNet神经网络和相关后处理得到道路概率图。其次，通过对左右图像中的重叠区域的道路概率图进行二值化来确定优选道路区域（PRA）。PRA是接缝线交叉的优先区域。最后，通过Dijkstra的最短路径算法确定最终的接缝线，该算法在像素级上使用二进制最小堆实现。三组数据集的实验结果表明了该方法的优越性。与已有的两种方法相比，该方法得到的接缝线穿过不太明显的物体，主要沿着道路。在计算效率方面，该方法也具有较高的效率。
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 (PRAs) 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.