Automatic determination of landmarks ia a challenge for image registration, especially, in an unfriendly and noisy environment such as the battlefield and with feature inconsistency between multiple sensors. In the contour-based multi-sensor image registration, the free-form curve matching does not require explicit feature correspondence. However, the approach can fail when percentage of outliers is too high. Human intervention is still needed sometimes to select good features. We introduce, in this paper, two new approaches to improve the robustness of feature extraction for automatic image registration. The method is based on the fact that the features must be robust to changes between the two images. The feature extraction proposed in this paper is divided into two steps. First of all, a method is presented in order to extract the horizon line by method of edge tracking. The horizon line is the longest contour in a ground image, but can be fragmented by noise or clutter. We fill the horizon line gaps by means of an association of regional and contour information that makes the edge tracking robust to noise. Secondly, a course curve matching is proposed in order to reduce the number of outliers drastically. The experimental results will be shown for the robust and automatic visible/far infrared battlefield image registration.