Characterization of signal perturbation using voting based curve fitting for multispectral images

Signal degradation impacts the final quality of images acquired using remote sensing radiometer. The effectiveness of a restoration algorithm strongly depends on two main factors: an accurate model of the disturbs introduced by the acquisition device and adaptation of the filtering method to image content. In this paper we target the first factor, by providing a solution for characterizing multispectral image signal degradation. A framework for estimating signal disturbs from heterogeneous sets of multispectral images is presented jointly with a voting-based technique for determining the best coefficients of the fitting equation. Tests conducted on multispectral images confirm the effectiveness of the proposed approach.