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Classification of Remotely Sensed Data Utilising the Autocorrelation between Spatio-Temporal Neighbours
Department of Mathematical Statistics, Umeå University, Umeå, Sweden.
1997 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The subject of this thesis is methods for classifying land using satellite images, and adherent parameter estimation. A satellite image consists of a set of pixels where measurements of spectral intensities are observed. Based on these spectral intensities, each pixel is assigned a class. The classification methods considered in this thesis are based on Bayesian decision theory.

Accounting for spatial and temporal dependence is of importance for classifying land. One such classification method is an autocorrelation method where the observed intensities are assumed to consist of the true intensities and some autocorrelated noise. In order to include temporal dependence, the spatial autocorrelation methods is extended to comprise information about temporal neighbours.

For applying classification methods it is necessary to estimate adherent parameters. The spatial autocorrelation parameter relies on the noise components, which are unobservable. An autocorrelation estimator based on Maximum-Likelihood estimates of autocovariances is introduced. This estimator is based on components that are differences between intensities from pixels taken on two different occasions over the same area. By doing so, this problem is easier to handle. Asymptotic properties such as strong consistency and asymptotic normality are proved for this estimator.

An efficient implementation algorithm for the autocorrelation methods is given which is necessary given the large amount of computation required. The suggested spatio-temporal autocorrelation method and some other classification methods are applied to real Landsat TM data. The results of the classifications were evaluated using data obtained through a field inventory. The conclusion from this study was that the spatio-temporal autocorrelation method performed best.

Place, publisher, year, edition, pages
Umeå: Umeå University , 1997. , p. 27
Keywords [en]
Image classification, strong mixing, spatio-temporal model, autocorrelation model, autocorrelation estimator, robustness aspects, strong consistency, asymptotic normality, evaluation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-66563ISBN: 91-7191-316-5 (print)OAI: oai:DiVA.org:oru-66563DiVA, id: diva2:1197185
Available from: 2018-05-07 Created: 2018-04-12 Last updated: 2018-05-07Bibliographically approved
List of papers
1. Contextual classification using multi-temporal Landsat TM data
Open this publication in new window or tab >>Contextual classification using multi-temporal Landsat TM data
1993 (English)Licentiate thesis, monograph (Other academic)
Place, publisher, year, edition, pages
Department of Statistics, University of Umeå, 1993. p. 72
Series
Statistical research report / University of Umeå, ISSN 0348-0399 ; 5
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-66349 (URN)
Available from: 2018-04-11 Created: 2018-04-04 Last updated: 2018-05-07Bibliographically approved
2. An alternative estimation method for spatial autocorrelation parameters
Open this publication in new window or tab >>An alternative estimation method for spatial autocorrelation parameters
1996 (English)Report (Other academic)
Place, publisher, year, edition, pages
Umeå: Department of Mathematical Statistics, Umeå University, 1996. p. 31
Series
Research Report, ISSN 1401-730X ; 4
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-66352 (URN)
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-12Bibliographically approved
3. Asymptotic normality of a spatial autocorrelation estimator
Open this publication in new window or tab >>Asymptotic normality of a spatial autocorrelation estimator
1997 (English)Report (Refereed)
Place, publisher, year, edition, pages
Umeå: Department of Mathematical Statistics, Umeå University, 1997. p. 16
Series
Research report, ISSN 1401-730X ; 1997:4
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-66351 (URN)
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-12Bibliographically approved
4. Efficient implementation of some contextual classification methods
Open this publication in new window or tab >>Efficient implementation of some contextual classification methods
1995 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 10, no 4, p. 327-338Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Heidelberg: Physica Verlag, 1995
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-66341 (URN)A1995TG05800002 ()
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-12Bibliographically approved
5. Comparing some contextual classification methods using Landsat TM
Open this publication in new window or tab >>Comparing some contextual classification methods using Landsat TM
1995 (English)Report (Other academic)
Place, publisher, year, edition, pages
Umeå University: Institute of Mathematical Statistics, 1995. p. 12
Series
Research report, ISSN 1400-2701 ; 7
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:oru:diva-66353 (URN)
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-12Bibliographically approved

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Flygare, Ann-Marie

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