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Fast estimation of spatially dependent temporal trends using Gaussian Markov Random fields

Bolin, David LU ; Lindström, Johan LU ; Eklundh, Lars LU and Lindgren, Finn LU (2009) In Computational Statistics & Data Analysis 53(8). p.2885-2896
Abstract
There is a need for efficient methods for estimating trends in spatio-temporal Earth Observation data. A suitable model for such data is a space-varying regression model, where the regression coefficients for the spatial locations are dependent. A second order intrinsic Gaussian Markov Random Field prior is used to specify the spatial covariance structure. Model parameters are estimated using the Expectation Maximisation (EM) algorithm, which allows for feasible computation times for relatively large data sets. Results are illustrated with simulated data sets and real vegetation data from the Sahel area in northern Africa. The results indicate a substantial gain in accuracy compared with methods based on independent ordinary least squares... (More)
There is a need for efficient methods for estimating trends in spatio-temporal Earth Observation data. A suitable model for such data is a space-varying regression model, where the regression coefficients for the spatial locations are dependent. A second order intrinsic Gaussian Markov Random Field prior is used to specify the spatial covariance structure. Model parameters are estimated using the Expectation Maximisation (EM) algorithm, which allows for feasible computation times for relatively large data sets. Results are illustrated with simulated data sets and real vegetation data from the Sahel area in northern Africa. The results indicate a substantial gain in accuracy compared with methods based on independent ordinary least squares regressions for the individual pixels in the data set. Use of the EM algorithm also gives a substantial performance gain over Markov Chain Monte Carlo-based estimation approaches. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Computational Statistics & Data Analysis
volume
53
issue
8
pages
2885 - 2896
publisher
Elsevier
external identifiers
  • wos:000265571000010
  • scopus:62849105642
ISSN
0167-9473
DOI
10.1016/j.csda.2008.09.017
project
MERGE
language
English
LU publication?
yes
id
248039dd-a96d-4a8b-9bbf-9e75e166bf82 (old id 1397790)
date added to LUP
2009-05-19 14:33:52
date last changed
2017-04-02 03:33:49
@article{248039dd-a96d-4a8b-9bbf-9e75e166bf82,
  abstract     = {There is a need for efficient methods for estimating trends in spatio-temporal Earth Observation data. A suitable model for such data is a space-varying regression model, where the regression coefficients for the spatial locations are dependent. A second order intrinsic Gaussian Markov Random Field prior is used to specify the spatial covariance structure. Model parameters are estimated using the Expectation Maximisation (EM) algorithm, which allows for feasible computation times for relatively large data sets. Results are illustrated with simulated data sets and real vegetation data from the Sahel area in northern Africa. The results indicate a substantial gain in accuracy compared with methods based on independent ordinary least squares regressions for the individual pixels in the data set. Use of the EM algorithm also gives a substantial performance gain over Markov Chain Monte Carlo-based estimation approaches.},
  author       = {Bolin, David and Lindström, Johan and Eklundh, Lars and Lindgren, Finn},
  issn         = {0167-9473},
  language     = {eng},
  number       = {8},
  pages        = {2885--2896},
  publisher    = {Elsevier},
  series       = {Computational Statistics & Data Analysis},
  title        = {Fast estimation of spatially dependent temporal trends using Gaussian Markov Random fields},
  url          = {http://dx.doi.org/10.1016/j.csda.2008.09.017},
  volume       = {53},
  year         = {2009},
}