Automatic relative radiometric normalization using iteratively weighted least square regression

Relative radiometric normalization among multiple remotely sensed images is an important step of preprocessing for applications such as change detection and image mosaicking. In this paper we present a new automatic normalization approach that uses the iteratively weighted least square regression technique. This approach does not require selection of the pseudo-invariant features beforehand as in some other traditional methods, and is robust to outliers since it adaptively places different weights on different pixels according to their probabilities of no-change. This approach is mainly applicable to cases where primary spectral differences between the two images are caused by variations in imaging conditions rather than phenological cycle or land cover changes. The effectiveness of this approach was demonstrated by two experiments using both artificially constructed data and remotely sensed images respectively. The experimental result seems promising and our approach shows accuracy comparable to normalization methods.