顾及局部相对几何变形改正的影像匹配和空三逐步精化方法

A Stepwise Refinement Method for Image Matching and Aerotriangulation Using Correction of Local Relative Geometric Distortion

  • 摘要: 针对海拔落差较大地区无人机影像匹配和空三加密精度较低的问题,在具备初始空三姿态参数和概略地面模型的基础上,提出了一种顾及局部相对几何变形改正的影像匹配和空三逐步精化方法。该方法综合考虑了立体像对相对倾斜和地形起伏引起的影像相对变形,在传统空三基础上引入顾及局部相对几何变形改正的单点精确匹配算法,使得影像匹配和定向相辅相成,从而提高了影像匹配和空三加密的精度和可靠性。实验分析证明,该算法能较好地改正影像相对几何变形,强化空三网型结构和进一步提高空三精度,适用于地形高差变化较大的低空无人机影像空中三角测量。在效率方面,由于借助概略地面模型进行预测,局部相对几何变形改正的影像范围相对有限,因此算法基本满足实际生产需求。若考虑到每个连接点使用的单点精确匹配具备很好的独立性,并行化算法将可以进一步提高效率。

     

    Abstract: Aiming at the difficulties of image matching and aerotriangulation for unmanned aerial vehical image in the area with high altitude drop, this paper presents a stepwise refinement method for image matching and aerotriangulation using correction of local relative geometric distortion (CLRGD) which is based on the initial aerotriangulation and rough terrain model. It takes into account the image relative distortion caused by relative tilt of stereo image pairs and topographic relief in which an algorithm for single point accurate matching considering CLRGD has been adopted in the process of normal aerotriangulation. Then image matching and orientation can complement each other so as to improve the accuracy and reliability of matching and aerotriangulation. Experimental analysis proves that the algorithm can do well in the image relative geometric distortion, strengthen the regional network and further improve the accuracy of the aerotriangulation in the area with high altitude drop. In terms of efficiency, the image range of CLRGD is relatively limited due to the use of rough terrain model, so the algorithm can basically meets the actual production needs. If considering that the accurate single point matching used by each tie point has good independence, a parallel algorithm can further improve the efficiency.

     

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