Decomposition method of raster geographic data based on parallel computing
Decomposition method of raster geographic data based on parallel computing
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DOI：
10.1109/geoinformatics.2012.6270298
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
Zhibing Jin;Yingxia Pu;Jie-chen Wang;Jingsong Ma;Gang Chen
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文献类型：
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作者：
Zhibing Jin;Yingxia Pu;Jie-chen Wang;Jingsong Ma;Gang Chen
本文主要研究了基于并行计算的栅格地理数据分解方法。首先，构建了栅格地理数据的计算转换模型；然后，我们设计了一个计算实验来验证计算转换模型，并评估k-NN分类算法的性能。并行计算实验结果表明，该模型可以将异构空间计算域表示分解为平衡的计算任务集；基于转换模型的并行化k-NN分类算法的加速性能优于传统方法。
The paper mainly studied decomposition method of raster geographic data based on parallel computing. Firstly, we structured computational transformation model of raster geographic data; Then, we designed a computational experiment to validate the computational transformation model and evaluate the performance of k-NN classification algorithm. Results of parallel computational experiment show that the model can be applied to decompose a heterogeneous spatial computational domain representation into a balanced set of computing tasks; the speedup performance of parallelizing k-NN classification algorithm based on the transformation model is superior to the results from traditional method.