Automatic normalization of satellite images using unchanged pixels within urban areas

Radiometric normalization is often required in remote sensing image analysis particularly in land change analysis. The normalization minimizes different imaging condition effects in analysis and rectifies radiometry of images in such a way as if they have been acquired at the same imaging conditions. Relative radiometric normalization which is normally applied in image preprocessing stage does not remove all unwanted effects. In this paper, an automatic normalization method has been developed based on regression applied on unchanged pixels within urban areas. The proposed method is based on efficient selection of unchanged pixels through image difference histogram modeling using available spectral bands and calculation of relevant coefficients for dark, gray and bright pixels in each band. The coefficients are applied to produce the normalized image. The idea has been implemented on two TM image datasets. The capability of the approach in taking into account the imaging condition differences and effectively excluding real land change pixels from the normalization process has shown better performance in the evaluation stage.