ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2016, Vol. 53 ›› Issue (12): 2801-2815.doi: 10.7544/issn1000-1239.2016.20150384

• 图形图像 • 上一篇    下一篇

一种基于MPS和ISOMAP的空间数据重建方法

杜奕1,张挺2,黄涛3   

  1. 1(上海第二工业大学工学部 上海 201209); 2(上海电力学院计算机科学与技术学院 上海 200090); 3(中国科学技术大学近代力学系 合肥 230027) (duyi@sspu.edu.cn)
  • 出版日期: 2016-12-01
  • 基金资助: 
    中国科学院战略性先导科技专项课题(XDB10030402);中国石油与中国科学院重大战略合作项目(2015A-4812);国家自然科学基金项目(41672114);上海市自然科学基金项目(16ZR1413200);上海第二工业大学校级重点学科建设计算机科学与技术项目(XXKZD1604)

A Reconstruction Method of Spatial Data Using MPS and ISOMAP

Du Yi1, Zhang Ting2, Huang Tao3   

  1. 1(College of Engineering, Shanghai Polytechnic University, Shanghai 201209); 2(College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai 200090); 3(Department of Modern Mechanics, University of Science and Technology of China, Hefei 230027)
  • Online: 2016-12-01

摘要: 在空间数据重建过程中,条件数据对重建结果影响较大,在仅有少量条件数据的情况下,重建结果常常出现较多的不确定性,此时适合采用不确定性插值方法重建空间数据.作为目前不确定性插值的主流方法之一,多点信息统计法(multiple-point statistics, MPS)可以从训练图像提取模式的本质特征,然后将这些特征复制到待模拟区域.由于传统采用线性降维的MPS方法无法有效处理非线性数据,因此将等距特征映射(isometric mapping, ISOMAP)应用到MPS方法,以实现对非线性数据的降维.提出基于MPS和ISOMAP的空间数据重建方法,通过模式库构建、模式降维、模式分类、模式提取等步骤能够较为准确地重构出未知的空间数据,为MPS处理非线性空间数据提供了新思路.实验结果表明:该方法所重建的空间数据具有与训练图像相似的结构特征.

关键词: 训练图像, 多点信息统计法, 模式, 等距特征映射, 降维

Abstract: Conditional data influence the reconstructed results greatly in the reconstruction of spatial data. Reconstructed results often show a number of uncertainties when only sparse conditional data are available, so it is suitable to use indefinite interpolation to reconstruct spatial data. As one of the main indefinite interpolation methods, multiple-point statistics (MPS) can extract the intrinsic features of patterns from training images and copy them to the simulated regions. Because the traditional MPS methods using linear dimensionality reduction are not suitable to deal with nonlinear data, isometric mapping (ISOMAP) is combined with MPS to address the above issues. A method using MPS and ISOMAP for the reconstruction of spatial data is proposed for the accurate reconstruction of unknown spatial data by constructing pattern dataset, dimensionality reduction of patterns, classification of patterns and extraction of patterns, which has provided a new idea for dealing with nonlinear spatial data by MPS. The experimental results show that the structural characteristics of reconstructed results using this method are similar to those of training images.

Key words: training image, multiple-point statistics (MPS), pattern, isometric mapping (ISOMAP), dimensionality reduction

中图分类号: