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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1397790
- author
- Bolin, David
LU
; Lindström, Johan
LU
; Eklundh, Lars
LU
and Lindgren, Finn
LU
- organization
- publishing date
- 2009
- 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
- Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling
- language
- English
- LU publication?
- yes
- id
- 248039dd-a96d-4a8b-9bbf-9e75e166bf82 (old id 1397790)
- date added to LUP
- 2016-04-01 12:20:02
- date last changed
- 2023-09-02 04:01:15
@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}},
doi = {{10.1016/j.csda.2008.09.017}},
volume = {{53}},
year = {{2009}},
}