一种基于带权A*搜索算法的正射影像镶嵌线网络优化方法

提出了一种基于带权A*搜索算法的镶嵌线网络优化方法。首先，利用标准Voronoi图生成初始镶嵌线网络；然后，利用测区的数字表面模型（digital surface model，DSM）数据生成对应的高程梯度图（也称为边缘图）；再对初始镶嵌线网络的节点进行自动调整，将位于建筑物上的节点移动至附近的地面；最后，利用一种带权A*搜索算法，结合高程梯度图，对初始镶嵌线网络中的每一条镶嵌线进行智能优化，避开建筑物或者高差变化大的区域，获得最优的镶嵌线网络。利用3组真实的无人机数据对该方法进行实验，初步结果表明，该方法适用于排列不规则的测区，可有效优化镶嵌线网络，镶嵌线可自动避开大部分城区建筑物以及山区的山脊等，对城区以及山区影像都可得到高质量的正射影像。实验结果表明，对于第1组数据，此方法得到的结果在镶嵌线的选取上要优于商业软件OrthoVista。

A Novel Seam-Line Network Optimization Method Using the Weighted A* Algorithm for UAV Imagery

An automatic optimization method based on weighted A* search algorithm is proposed. The main process can be divided into four steps:the first step is to generate the initial seam-line network using standard Voronoi map; and then use the digital surface model (DSM) data to generate the corresponding elevation gradient map (also known as edge map); then the initial nodes of the seam-line network are automatically adjusted, the nodes located on the building are moved to the near ground; finally a weighted A* algorithm combined with the elevation gradient map are used to pilot all the seam-lines to avoid high buildings, and obtain the optimal seam-line network. This method is tested with three real UAV dataset. Preliminary result has shown that our method is suitable for unmanned aerial vehicle imagery, and acceptable mosaic image is produced. The result is proved to be better than the result of OrthoVista for dataset 1.