基于RASM的紧支撑径向基函数自适应并行地形插值方法

发布时间：2018-05-07 08:33

  本文选题：地形重建 + 区域分解　； 参考：《武汉大学学报(信息科学版)》2017年09期

【摘要】：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。以某地区数字高程模型(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。
[Abstract]:Rapid and accurate reconstruction of terrain to generate digital elevation model is an important research content of geographic information expression. Radial basis function (RBF) is a spatial interpolation method with better interpolation performance. It is especially suitable for reconstruction of complex terrain model, but with the increase of the number of known topographic sampling points, the solution speed of RBF interpolation model becomes slower, and the interpolation matrix is too large, which leads to the difficulty and even failure of the interpolation model. In order to solve this problem, terrain interpolation is based on domain decomposition and Schwartz parallel principle. A global interpolation matrix based on all terrain sampling data is constructed by compact supported radial basis function (RBF). The compact support radius of subregion CSRBF interpolation nodes is solved adaptively. Based on restricted additive Schwarz method RASM (restricted additive Schwarz method), the interpolation matrices of local subregions are solved using a multi-core parallel architecture. The interpolation experiment based on the digital elevation model (DEM) data of a certain area shows that the proposed method can reconstruct the large-scale terrain data accurately and has a high efficiency.
【作者单位】： 南京师范大学虚拟地理环境教育部重点实验室;南京师范大学江苏省地理信息资源开发与利用协同创新中心;云南师范大学旅游与地理科学学院;
【基金】：国家自然科学基金(41271383,41371374,41471102)~~
【分类号】：P208
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本文编号：1856206