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快速、准确地对地形进行重建以生成数字高程模型是地理信息表达的重要研究内容,径向基函数(radial basis function,RBF)作为一种插值性能较优的空间插值方法,特别适合于重建复杂的地形模型,但随着已知地形采样点数量的增加,RBF插值模型求解速度变慢,同时插值矩阵过于庞大而导致插值模型求解困难甚至求解失败。针对这个问题,本文基于区域分解和施瓦兹并行原理进行地形插值,以紧支撑径向基函数(compact support RBF,CSRBF)构建基于所有地形采样数据的全局插值矩阵,并自适应求解子区域CSRBF插值节点紧支撑半径,基于限制性加性施瓦兹方法(restricted additive schwarz method,RASM)采用多核并行架构对各局部子区域的插值矩阵进行求解。以某地区DEM数据进行插值实验,结果表明,本文方法能够对大规模地形数据进行准确重建,并且具有较高的求解效率。
To reconstruct the terrain quickly and accurately to generate the digital elevation model is an important research topic of geographic information expression. As a spatial interpolation method with better interpolation performance, radial basis function (RBF) is particularly suitable for reconstruction of complex However, as the number of known terrain sampling points increases, the RBF interpolation model is slowed down and the interpolation matrix is too large, which makes it difficult or even unsuccessful to solve the interpolation model. In order to solve this problem, this paper, based on the regional decomposition and Schwartz’s parallel principle, performs the terrain interpolation. The global interpolation matrix based on all terrain sampling data is constructed with compact support radial basis function (CSRBF), and the sub-region CSRBF The interpolation nodes are tight support radius, and the multi-core parallel architecture is used to solve the interpolation matrix of each sub-region based on the restricted additive Schwarz method (RASM). The interpolation experiment with DEM data in a certain area shows that the proposed method can accurately reconstruct large-scale topographic data and has high solution efficiency.