Abstract:Registration of satellite imagery is key step for remote sensing applications like global change detection, image fusion, and feature classification. Manual registration is very time consuming and repetitive, an automatic way for image registration is needed. In this paper such a method for image registration is proposed which is fully automatic and computationally efficient unlike global registration in which control points are selected manually. There is a preregistration process and a fine-tuning process. The first stage includes feature selection and description using SURF and an outlier removal procedure using RANSAC. This gets the optimizer in the fine-tuning process a near optimal solution. Next, the fine-tuning process is implemented by the maximization of mutual information. The proposed scheme is tested on various remote sensing images taken at different situations (multispectral, multisensor, and multitemporal) with the affine transformation model. It is demonstrated experimentally that proposed scheme is fully automatic and much efficient than the global registration. SURF is mostly used algorithm as it is the fastest descriptor. This paper shows that by increasing the matching points, image registration can be accurately done.