李佳田, 贾成林, 牛一如, 阿晓荟, 高鹏, 晏玲. 一种求解单像空间后方交会的监督学习方法[J]. 武汉大学学报 ( 信息科学版), 2019, 44(8): 1144-1152. DOI: 10.13203/j.whugis20170292
引用本文: 李佳田, 贾成林, 牛一如, 阿晓荟, 高鹏, 晏玲. 一种求解单像空间后方交会的监督学习方法[J]. 武汉大学学报 ( 信息科学版), 2019, 44(8): 1144-1152. DOI: 10.13203/j.whugis20170292
LI Jiatian, JIA Chenglin, NIU Yiru, A Xiaohui, GAO Peng, YAN Ling. A Supervised Learning Method for Solving Space Resection of Single Image[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1144-1152. DOI: 10.13203/j.whugis20170292
Citation: LI Jiatian, JIA Chenglin, NIU Yiru, A Xiaohui, GAO Peng, YAN Ling. A Supervised Learning Method for Solving Space Resection of Single Image[J]. Geomatics and Information Science of Wuhan University, 2019, 44(8): 1144-1152. DOI: 10.13203/j.whugis20170292

一种求解单像空间后方交会的监督学习方法

A Supervised Learning Method for Solving Space Resection of Single Image

  • 摘要: 单像空间后方交会可描述为非线性最小二乘问题,不可导、法方程系数矩阵病态以及陷入局部极值是造成其数值过程不收敛的主要原因。不同地区的控制点空间分布不具相似性,若把同一地区同一组控制点之下数张已知外方位元素的像片看作一个样本集,则在给定每个外方位元素初值的前提下,可通过监督学习方法求取外方位元素的整体下降方向;而对于单像空间后方交会中因前述原因不收敛的情况,则可采用整体下降方向近似解算。以此为出发点,提出一种单像空间后方交会求解的监督学习方法,主要过程是:①训练阶段,利用监督学习过程,对同一测区内不同姿态像片所组成的样本集进行整体外方位元素的求解,得到该测区外方位元素的整体下降方向集合;②测试阶段,对该测区的任意像片,给定外方位元素的初值,直接采用训练阶段得到的整体下降方向集合进行外方位元素的迭代求解。对比试验表明,该方法在数值过程收敛性与初值依赖性上均表现出较强的优势,并能克服欧拉角法因法方程系数矩阵病态而无法收敛的情况。

     

    Abstract: The space resection of single image can be described as a problem of non-linear least squares, and the non-derivative, ill-conditioned coefficient matrix of normal equation and local extremum are main reasons for non-convergence in its numerical procedure. The spatial distribution of control points in different regions is not similar. If we put down the multiple images and have known their exterior orientation elements regarding the same control points under the same region, as a sample set, the overall descent direction can be obtained by supervised learning under the circumstance that every initial values of exterior orientation elements have been given. What's more, in the case of non-convergence in original space resection of single image because of the reasons mentioned before, it can be approximately solved by using the overall descent direction. From this angle, a supervised learning method for solving space resection of single im age is proposed. The process mainly includes:①Training stage, in which supervised learning process is utilized and the descend direction set of exterior orientation elements is obtained by solving the overall exterior orientation elements of images set with different attitude in the same survey area. ②Testing stage, in which for any image in study area, the exterior orientation elements can be calculated iteratively if the initial value and the descend direction set were given from the process of supervised training. Experimental results show that the method in this paper is more efficient in numerical procedure convergence and dependence of the initial value than the current there realized. Besides, it can overcome the non-convergence of Euler angles caused by the ill-conditioned coefficient matrix of normal equation, which is essentially the gradient matrix.

     

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