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Computational statistics with environmental and remote sensing applications

Teterukovskiy, Alexei (2003). Computational statistics with environmental and remote sensing applications. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta Universitatis agriculturae Sueciae. Silvestria, 1401-6230 ; 277
ISBN 91-576-6511-7
[Doctoral thesis]

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Abstract

This thesis deals with application of several methods of computational statistics to the estimation of parameters in various models in remote sensing and environmental applications. The considered methods and applications are the following: - mapping of the spatial distribution of reindeer in the case of the incomplete ground survey by the Gibbs sampler - detection of small-sized tracks in aerial photos and satellite images with help of the Gibbs sampler - contextual classification of multispectral images with spatially correlated noise using Markov chain Monte Carlo methods and Markov random field prior - maximum likelihood estimation of the parameters of forest growth models with measurement errors - maximum spacing estimation based on Dirichlet tesselation for univariate and multivariate observations. In paper I we try to answer the following question. In mapping of animal distributions what is the minimum adequate number of plots one must survey to maintain a high accuracy of prediction? We use the Gibbs sampler to simulate the data for unsurveyed plots, and then use the simulated data to fit the autologistic model. In paper II we propose an algorithm for extracting small tracks from remotely sensed images. We specify several prior distributions of varying complexity, and calculate a maximum a posteriori estimate of the map of tracks using the Gibbs sampler. Paper III deals with classification of multispectral imagery in presence of autocorrelated noise. By means of simulation study we show how the classification results of conventional algorithms can be improved by adopting the Markov random field prior model. In paper IV the forest growth model with measurement errors is introduced. We establish some asymptotic properties for maximum likelihood estimates of the parameters of this model. Paper V is devoted to maximum spacing estimation based on Dirichlet tesselation. We prove consistency of such maximum spacing estimate in univariate case and conjecture it holds in higher dimensions.

Authors/Creators:Teterukovskiy, Alexei
Title:Computational statistics with environmental and remote sensing applications
Series/Journal:Acta Universitatis agriculturae Sueciae. Silvestria (1401-6230)
Year of publishing :May 2003
Volume:277
Number of Pages:29
Papers/manuscripts:
NumberReferences
ALLI. Teterukovskiy, A. and Edenius, L. (2001) Effective field sampling for predicting the spatial distribution of reindeer (Rangifer tarandus) with heop of the Gibbs sampler. Submitted. II. Teterukovskiy, A. (2003) Detection of tracks in aerial photos by the Gibbs sampler. International journal of Pattern Recognition and Artificial Intelligence, Vol. 17, No. 1, pp. 1-16. III. Teterukovskiy, A., and Yu, J. (2002) Contextual reclassification of multispectral images: A Markov Random Field approach. Information Processes, Vol. 2, No. 1, pp. 12-21. IV. Teterukovskiy, A. and Ranneby, B. (2003) Maximum likelihood estimation in forest growth models with measurement errors. Submitted. V. Teterukovskiy, A. and Ranneby, B. (2003) Maximum spacing estimation based on Dirichlet tesselation. Manuscript.
Place of Publication:Umeå
Publisher:Department of Forest Economics, Swedish University of Agricultural Sciences
ISBN for printed version:91-576-6511-7
ISSN:1401-6230
Language:English
Publication Type:Doctoral thesis
Full Text Status:Public
Agris subject categories.:U Auxiliary disciplines > U40 Surveying methods
Subjects:Not in use, please see Agris categories
Agrovoc terms:remote sensing, aerial surveying, classification, spatial distribution, reindeer, forests, growth, simulation models
Keywords:Gibbs sampler, detection, classification, Markov random field, forest growth model, maximum spacing estimate
URN:NBN:urn:nbn:se:slu:epsilon-48
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-48
ID Code:285
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Economics
Deposited By: Alexei Teterukovskiy
Deposited On:12 May 2003 00:00
Metadata Last Modified:11 Jun 2015 08:46

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