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http://dx.doi.org/10.7848/ksgpc.2018.36.6.515

Relative Radiometric Normalization for High-Spatial Resolution Satellite Imagery Based on Multilayer Perceptron  

Seo, Dae Kyo (Dept. of Advanced Technology Fusion, Konkuk University)
Eo, Yang Dam (Dept. of Technology Fusion Engineering, Konkuk University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.36, no.6, 2018 , pp. 515-523 More about this Journal
Abstract
In order to obtain consistent change detection result for multi-temporal satellite images, preprocessing must be performed. In particular, the preprocessing related to the spectral values can be performed by the radiometric normalization, and relative radiometric normalization is generally utilized. However, most relative radiometric normalization methods assume a linear relationship between the two images, and nonlinear spectral characteristics such as phenological differences are not considered. Therefore, this study proposes a relative radiometric normalization which assumes nonlinear relationships that can perform compositive normalization of radiometric and phenological characteristics. The proposed method selects the subject and reference images, and then extracts the radiometric control set samples through the no-change method. In addition, spectral indexes as well as pixel values are extracted in order to consider sufficient information, and modeling of nonlinear relationships is performed through multilayer perceptron. Finally, the proposed method is compared with the conventional relative radiometric normalization methods, which shows that the proposed method is visually and quantitatively superior.
Keywords
Relative Radiometric Normalization; Phenological Normalization; Multilayer Perceptron; Nonlinear;
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Times Cited By KSCI : 1  (Citation Analysis)
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