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Automatic radiometric normalization with genetic algorithms and a Kriging model

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Title: Automatic radiometric normalization with genetic algorithms and a Kriging model
Authors: Liu, Shou-Heng1, Lin, Ching-Weei1 chingwee@mail.ncku.edu.tw, Chen, Yie-Ruey2, Tseng, Chih-Ming2
Source: Computers & Geosciences. Jun2012, Vol. 43, p42-51. 10p.
Subjects: Radiation measurements, Genetic algorithms, Kriging, Remote-sensing images, Regression analysis, Interpolation, High resolution imaging, Quantitative research, Spatial variation
Abstract: Abstract: An automatic procedure of radiometric normalization is proposed for multi-temporal satellite image correction, with a modified genetic algorithm (GA) regression method and a spatially variant normalization model using the Kriging interpolation. The proposed procedure was tested on a synthetic altered image and an image pair from FORMOSAT-2; the results show that the GA method is more robust than the conventional PCA methods in high-resolution imaging, and that different regression-error evaluation models have different sensitivities to the linear regression parameters. A statistical comparison demonstrates that 1-km sampling spacing is able to successfully achieve the parameter spatial variation. Error validation on FORMOSAT-2 image pair shows it is a decent combination of radiometric normalization with GA estimation and a spatially variant parameter normalization model. [Copyright &y& Elsevier]
Copyright of Computers & Geosciences is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="JN" term="%22Computers+%26+Geosciences%22">Computers & Geosciences</searchLink>. Jun2012, Vol. 43, p42-51. 10p.
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  Data: Abstract: An automatic procedure of radiometric normalization is proposed for multi-temporal satellite image correction, with a modified genetic algorithm (GA) regression method and a spatially variant normalization model using the Kriging interpolation. The proposed procedure was tested on a synthetic altered image and an image pair from FORMOSAT-2; the results show that the GA method is more robust than the conventional PCA methods in high-resolution imaging, and that different regression-error evaluation models have different sensitivities to the linear regression parameters. A statistical comparison demonstrates that 1-km sampling spacing is able to successfully achieve the parameter spatial variation. Error validation on FORMOSAT-2 image pair shows it is a decent combination of radiometric normalization with GA estimation and a spatially variant parameter normalization model. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Computers & Geosciences is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.cageo.2011.12.016
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 42
    Subjects:
      – SubjectFull: Radiation measurements
        Type: general
      – SubjectFull: Genetic algorithms
        Type: general
      – SubjectFull: Kriging
        Type: general
      – SubjectFull: Remote-sensing images
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      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Interpolation
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      – SubjectFull: High resolution imaging
        Type: general
      – SubjectFull: Quantitative research
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      – SubjectFull: Spatial variation
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      – TitleFull: Automatic radiometric normalization with genetic algorithms and a Kriging model
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            NameFull: Liu, Shou-Heng
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            NameFull: Lin, Ching-Weei
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            NameFull: Chen, Yie-Ruey
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            NameFull: Tseng, Chih-Ming
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            – D: 01
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              Text: Jun2012
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              Y: 2012
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