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Gauss-Newton Method for DEM Co-registration

机译:高斯-牛顿法进行DEM共注册

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摘要

Digital elevation model (DEM) co-registration is one of the hottest research problems, and it is the critical technology for multi-temporal DEM analysis, which has wide potential application in many fields, such as geological hazards. Currently, the least-squares principle is used in most DEM co-registration methods, in which the matching parameters are obtained by iteration; the surface co-registration is then accomplished. To improve the iterative convergence rate, a Gauss-Newton method for DEM co-registration (G-N) is proposed in this paper. A gradient formula based on a gridded discrete surface is derived in theory, and then the difficulty of applying the Gauss-Newton method to DEM matching is solved. With the G-N algorithm, the surfaces approach each other along the maximal gradient direction, and therefore the iterative convergence and the performance efficiency of the new method can be enhanced greatly. According to experimental results based on the simulated datasets, the average convergence rates of rotation and translation parameters of the G-N algorithm are increased by 40 and 15% compared to those of the ICP algorithm, respectively. The performance efficiency of the G-N algorithm is 74.9% better.
机译:数字高程模型(DEM)共注册是最热门的研究问题之一,并且是多时相DEM分析的关键技术,在地质灾害等许多领域具有广泛的潜在应用。当前,最小二乘原理在大多数DEM共注册方法中使用,其中匹配参数是通过迭代获得的。然后完成表面共配准。为了提高迭代收敛速度,本文提出了一种高斯-牛顿DEM共注册方法(G-N)。从理论上推导了基于网格离散面的梯度公式,解决了将高斯-牛顿法应用于DEM匹配的难点。利用G-N算法,曲面沿最大梯度方向彼此靠近,因此可以大大提高新方法的迭代收敛性和性能效率。根据基于模拟数据集的实验结果,与ICP算法相比,G-N算法的旋转和平移参数的平均收敛速度分别提高了40%和15%。 G-N算法的性能效率提高了74.9%。

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