Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds
Intelligent optimization of seam-line finding for orthophoto mosaicking with LiDAR point clouds
复制标题
DOI：
10.1631/jzus.c1000235
复制
发表时间：
2011-05
期刊：
影响因子：
--
通讯作者：
Hongchao Ma;Jie Sun
中科院分区：
文献类型：
--
作者：
Hongchao Ma;Jie Sun
对机载光探测和测距(LiDAR)系统获取的图像进行了详细的研究，以找到最佳的拼接接缝线。被标记为障碍物的高地物体可以通过从过滤后的点云中描绘黑洞来识别，所述过滤后的点云通过对原始激光扫描数据集进行过滤而获得。提出了一种新颖的A*算法，该算法能够自动使配准图像中的缝线远离这些障碍物。该方法可以智能地优化拼接线的选择，提高正射影像的质量。首先利用模拟网格图像分析了不同启发式函数对路径规划的影响。获得了西北部中国地区西安、敦煌、长阳的激光雷达数据的三个子集。使用了包括像素强度、色调和纹理的定量方法。在敦煌、西安和长阳分别获得了9.4%、8.7%和9.8%的改善。
A detailed study was carried out to find optimal seam-lines for mosaicking of images acquired by an airborne light detection and ranging (LiDAR) system. High ground objects labeled as obstacles can be identified by delineating black holes from filtered point clouds obtained by filtering the raw laser scanning dataset. An innovative A* algorithm is proposed that can automatically make the seam-lines keep away from these obstacles in the registered images. This method can intelligently optimize the selection of seam-lines and improve the quality of orthophotos. A simulated grid image was first used to analyze the effect of different heuristic functions on path planning. Three subsets of LiDAR data from Xiüan, Dunhuang, and Changyang in Northwest China were obtained. A quantitative method including pixel intensity, hue, and texture was used. With our proposed method, 9.4%, 8.7%, and 9.8% improvements were achieved in Dunhuang, Xiüan, and Changyang, respectively.