Performance of mutual information similarity measure for registration of multitemporal remote sensing images

Abstract

Accurate registration of multitemporal remote sensing images is essential for various change detection applications. Mutual information has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of joint histograms to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new joint histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multitemporal remote sensing images. The experimental results show that higher order GPVE algorithms have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual-information-based image registration performed using the GPVE algorithm produces better registration consistency than the other two popular similarity measures, namely, mean squared difference (MSD) and normalized cross correlation (NCC), used for the registration of multitemporal remote sensing images.