一种新的空间数据不确定性重建方法

摘要

在重建空间数据时,如果条件数据较少甚至没有任何条件数据,重建结果常常出现较多的不确定性,此时适合采用基于统计原理的随机摸拟方法重建空间数据.多点信息统计法(Multiple-Point Statistics,MPS)是目前随机模拟的主流方法,它可以将训练图像中提取的本质特征复制到重建区域.由于传统采用线性降维的MPS无法较好处理非线性数据,而局部线性嵌入(Locally Linear Embedding, LLE)可以实现对非线性数据的降维,因此提出LLE与MPS相结合的空间数据不确定性重建方法.利用该方法对图像数据进行重建实验,实验结果证明该方法的有效性.%When reconstructing spatial data, if conditional data are sparse or even not existent, reconstructed results often show a lot of uncertainties, so it is appropriate to use stochastic simulation based on statistical theories to reconstruct spatial data. As one of the main stochastic simulation methods, multiple-point statistics (MPS) can copy the intrinsic features extracted from training images to the reconstructed regions. Because the traditional MPS methods using linear dimensionality reduction cannot effectively handle nonlinear data but locally linear embedding (LLE) can achieve dimensionality reduction of nonlinear data, an indefinite reconstruction method using LLE and MPS for spatial data is proposed. The experimental results for images show that the proposed method is practical.