Comparative analysis of algorithms for detecting structural changes in images

Algorithms are considered that solve the problem of detecting structural changes in multitemporal satellite images of the Earth’s surface. A computational method is formulated for comparing such algorithms; to this end, a special mathematical model of structural changes is introduced that allows one...

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Vydáno v:Pattern recognition and image analysis Ročník 25; číslo 4; s. 593 - 602
Hlavní autor: Kornilov, F. A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Moscow Pleiades Publishing 01.10.2015
Springer Nature B.V
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ISSN:1054-6618, 1555-6212
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Abstract Algorithms are considered that solve the problem of detecting structural changes in multitemporal satellite images of the Earth’s surface. A computational method is formulated for comparing such algorithms; to this end, a special mathematical model of structural changes is introduced that allows one to maximally approximate an experiment to real problems. Optimal parameters of operation of the algorithms are determined by this method, and the results of comparison of the performance of the algorithms are presented. Experiments on real data show that the algorithms proposed are suitable for practical application.
AbstractList Algorithms are considered that solve the problem of detecting structural changes in multitemporal satellite images of the Earth's surface. A computational method is formulated for comparing such algorithms; to this end, a special mathematical model of structural changes is introduced that allows one to maximally approximate an experiment to real problems. Optimal parameters of operation of the algorithms are determined by this method, and the results of comparison of the performance of the algorithms are presented. Experiments on real data show that the algorithms proposed are suitable for practical application.
Author Kornilov, F. A.
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Keywords image processing
mathematical morphology
structural changes
comparison of algorithms
Markov random fields
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PublicationTitle Pattern recognition and image analysis
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Supercomputer Center Uran of Krasovskii Institute of Mathematics and Mechanics. http://parallel.uran.ru
GonzalezR. C.WoodsR. E.Digital Image Processing1992
KornilovF. A.PerevalovD. S.A problem for recognizing structural differences for imagesAlgorithms and Software for Parallel Calculations. Collection of Scientific Papers20113756
AivazyanS. A.MkhitaryanV. S.Applied Statistics and Foundations of Econometrics1998MoscowYuniti
GreenD. M.SwetsJ. M.Signal Detection Theory and Psychophysics1966New YorkJohn Wiley and Sons
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– reference: BoreskovA. V.Parallel Calculations with GPU. Architectural and Software Model CUDA2012
– reference: LiS. Z.Markov Random Field Modeling in Image Analysis200931183.68691
– reference: OriginPro 8.0. http://www.originlab.com/
– reference: KornilovF. A.Algorithm for searching structural difference by using Markov random fieldsProc. Int. (43rd Russian) Youth School-Conf. Modern Problems in Mathematics2012YekaterinburgKrasovskii Institute of Mathematics and Mechanics311313
– reference: List of foreign geoinformation systems. http://en.wikipedia.org/wiki/List_of_geographic_information_syste ms_software
– reference: AivazyanS. A.MkhitaryanV. S.Applied Statistics and Foundations of Econometrics1998MoscowYuniti
– reference: GreenD. M.SwetsJ. M.Signal Detection Theory and Psychophysics1966New YorkJohn Wiley and Sons
– reference: GonzalezR. C.WoodsR. E.Digital Image Processing1992
– reference: KandobaI. N.KostrousovV. B.PerevalovD. S.The way to create mathematical methods, parallel algorithms and software for solving pattern analysis problems and control problems in the systems of highaccurate navigation and guidance for moving objectsProc. 13th All-Russian Conf. “Mathematical Methods for Pattern Recognition” (ÌÌÐR–13)2007335337
– reference: WinklerG.Image Analysis, Random Fields, and Dynamic Monte Carlo Methods: A Mathematical Introduction (Applications of Mathematics)1994
– reference: KornilovF. A.Algorithm for searching structural differences for imagesTr. Inst. Mat. Ural Branch RAS2013191144159
– reference: Pyt’evYu. P.ChulichkovA. I.Morphological Analysis for Images2010MoscowFizmatlit
– reference: KornilovF. A.PerevalovD. S.The way to use the regularizing morphological projector for the problems on searching the structural differencesProc. Sci.Tech. Conf. “Technical Vision in Control Systems-2012”2012265271
– reference: KornilovF. A.PerevalovD. S.A problem for recognizing structural differences for imagesAlgorithms and Software for Parallel Calculations. Collection of Scientific Papers20113756
– reference: KostousovV. B.PerevalovD. S.Problems on recognizing and localizing objects in imagesProc. Int. Conf. “Algorithmial Analysis of Nonstable Problems”2008YekaterinburgKrasovskii Institute of Mathematics and Mechanics136137
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Snippet Algorithms are considered that solve the problem of detecting structural changes in multitemporal satellite images of the Earth’s surface. A computational...
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SubjectTerms Algorithms
Analysis and Understanding of Images
Comparative analysis
Computer Science
Data compression
Image Processing and Computer Vision
Image processing systems
Mathematical models
Morphology
Pattern Recognition
Processing
Representation
Studies
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