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.
Key words
pattern /
multiple-point statistics /
nonlinear /
locally linear embedding /
reconstruction
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References
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Footnotes
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Funding
National Natural Science Foundation of China (No.41672114, No.41702148); National Natural Science Foundation of Shanghai Municipality, China (No.16ZR1413200); Major Strategic Cooperation Project of CNPC and CAS (No.2015A-4812); Chinese Academy of Sciences Strategic Pilot Project (No.XDB10030402); Science and Technology Project of Zhejiang Province (No.2017C33163); Fundamental Research Funds for the Central Universities (No.WK2090050038)
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