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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]
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Database: Engineering Source
Description
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]
ISSN:00983004
DOI:10.1016/j.cageo.2011.12.016