Automatic radiometric normalization with genetic algorithms and a Kriging model

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. oposed 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.