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Efficient implementation of some contextual classification methods
Umeå University, Umeå, Sweden.
Umeå University, Umeå, Sweden.
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. Vol. 10, no 4, p. 327-338
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:oru:diva-66341ISI: A1995TG05800002OAI: oai:DiVA.org:oru-66341DiVA, id: diva2:1195009
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-04-12Bibliographically approved
In thesis
1. Classification of Remotely Sensed Data Utilising the Autocorrelation between Spatio-Temporal Neighbours
Open this publication in new window or tab >>Classification of Remotely Sensed Data Utilising the Autocorrelation between Spatio-Temporal Neighbours
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
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:nbn:se:oru:diva-66563 (URN)91-7191-316-5 (ISBN)
Available from: 2018-05-07 Created: 2018-04-12 Last updated: 2018-05-07Bibliographically approved

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Flygare, Ann-Marie

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