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Change Detection in Multispectral Landsat Images Using Multiobjective Evolutionary Algorithm
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0001-7536-3349
Karatay University, TUR. (-)ORCID iD: -
2017 (English)In: IEEE Geoscience and Remote Sensing Letters, ISSN 1545-598X, E-ISSN 1558-0571, Vol. 14, no 3, 414-418 p., 10.1109/LGRS.2016.2645742Article in journal (Refereed) Published
Abstract [en]

In this letter, we propose a novel method for unsupervised change detection in multitemporal multispectral Landsat images using multiobjective evolutionary algorithm (MOEA). The proposed method minimizes two different objective functions using MOEA to provide tradeoff between each other. The objective functions are used for evaluating changed and unchanged regions of the difference image separately. The difference image is obtained by using the structural similarity index measure method, which provides combination of the comparisons of luminance, contrast, and structure between two images. By evolving a population of solutions in the MOEA, a set of Pareto optimal solution is estimated in a single run. To find the best solution, a Markov random field fusion approach is used. Experiments on semisynthetic and real-world data sets show the efficiency and effectiveness of the proposed method.

Place, publisher, year, edition, pages
IEEE, 2017. Vol. 14, no 3, 414-418 p., 10.1109/LGRS.2016.2645742
Keyword [en]
remote sensing, Change detection, image processing, Landsat images, multiobjective evolutionary algorithms (MOEAs)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-13978DOI: 10.1109/LGRS.2016.2645742ISI: 000395908600028OAI: oai:DiVA.org:bth-13978DiVA: diva2:1078219
Available from: 2017-03-02 Created: 2017-03-02 Last updated: 2017-04-03Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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