Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information.

Owing to significant geometric distortions and illumination differences, high precision and robust matching of multisource remote sensing images is a difficult task. To solve this, mutual information (MI)-based methods have been a preferred choice, as MI represents a measure of statistical dependence between the two images. However, MI only considers original grey information and neglects spatial information in the calculation of the probability distribution. In this paper, a novel similarity metric based on rotationally invariant regional mutual information (RIRMI) is proposed. The RIRMI metric is constructed by combining MI with a regional information based on the statistical relationship between rotationally invariant centre-symmetric local binary patterns of the images. The similarity metric based on RIRMI considers not only the spatial information, but the effect of the local grey variations and rotation changes on computing probability density function as well. The proposed method is tested on various simulated remote sensing images (5-30 m GSD) and real remote sensing images (2-30 m GSD) which are taken at different sensors, spectral bands, and times. Results verify that RIRMI is more robust and accurate than the common MI-based registration method. [ABSTRACT FROM AUTHOR]