Image registration is a major issue in the field of Remote Sensing because it provides a support for integrating information from two or more images into a model that represents our knowledge on a given application. It may be used for comparing the content of two segmented images captured by the same sensor at different times; but it also may be used for extracting and assembling information from images captured by various sensors corresponding to different modalities (optical, radar,). The registration of images from different modalities is a very difficult problem because data representations are different (e.g. vectors for multispectral images and scalar values for radar ones) but also, and especially, because an important part of the information is different from an image to another (e.g. hyperspectral signature and radar response). And precisely, any registration process is based, explicitly or not, on matching the common information in the two images. The problem we are interested in is to develop a generic approach that enables the registration of two images from different modalities when their spatial representations are related by a rigid transformation. This situation often occurs, and it requires a very robust and accurate registration process to provide the spatial correspondence. First, we show that this registration problem between images from different modalities can be reduced to a matching problem between binary images. There are many approaches to tackle this problem, and we give an overview of these approaches. But we have to take into account the specificity of the context in which we have to solve this problem: we must select those points of both images that are associated with the same information, and not the other ones, in order to process the pairing that will lead to the registration parameters. The approach we propose is a Hough-like method that induces a separation between relevant and non-relevant pairings, the Hough space being a representation of the rigid transformation parameters. In order to characterize the relevant items in each image, we propose a new primitive that provides a local representation of patterns in binary images. We give a complete description of this approach and results concerning various types of images to register.