A multi-sensor image registration approach based on long-edge-correlation

Some registration approaches can fail when percentage of outliers is too high in remote images. We introduce, in this paper, a new approach to improve the robustness of feature extration for automatic image registration. This method is based on long-edge correlation and consistency check. Long-edge correlation extracts a long edge as reference curve in order to increase the percentage of common feature in the edge maps, and consistent check reduce the number of outliers drastically. The proposed method based on comparison of HuiLi's correlation is a modified chain code correlation coefficient method. In addition, get more consistent-edge by improvement of Randrianarisoa method. The simulation experiments show the robust registration results of the method for images rich in long-edge. Another advantage of the method is rapid computational speed.