Optical remote sensing has limitations in obtaining images due to weather and environmental effects, so these limitations must be overcome to produce time-series image data. As an alternative to this, research are being conducted to simulate images at a specific time for which a specific image is needed. The purpose of this study is to improve the results of this process by preprocessing the input images of a multiple linear regression model alongside other remote sensing image simulation methods. Specifically, the input images, which are applied to a multi-linear regression equation, are preprocessed for phenological and radiometric normalization by a random forest regression model. The experimental results show that the proposed method is superior to the conventional methods both visually and quantitatively.


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    Titel :

    Analysis of Image Preprocessing Effects in a Landsat Image Simulation


    Weitere Titelangaben:

    KSCE J Civ Eng


    Beteiligte:
    Seo, Dae Kyo (Autor:in) / Eo, Yang Dam (Autor:in) / Paik, Geun Woo (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2020-07-01


    Format / Umfang :

    7 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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