## 1 Introduction
As analyzed by different authors, there is a critical need to develop automated techniques requiring little or no operator intervention to register multi-temporal and/or multisensorial images when higher accuracy is desired. Towards this goal, based on the nature of features used, automated registration techniques can be broadly grouped into area-based and feature-based methods. In area-based methods, sometimes called correlation-like or template matching methods, images are registered by selecting a number of windows with high-variance areas in the reference image, locating the corresponding windows in the sensed images by cross correlation, using the windows centers as control points to determine the registration parameters [10,11]. The limitations of the area-based methods originate in their basic idea. Firstly, the rectangular window, which is most often used, suits the registration of images which locally differ only by a translation. If images are deformed by more complex transformations, this type of window is not able to cover the same parts of the scene in the reference and sensed images. Another disadvantage of the area-based methods refers to the image intensities of the window content. There is high probability that a window containing a smooth area without any prominent details will be matched incorrectly with other smooth areas in the images. Feature-based methods, usually rely on establishing feature correspondence between two images. They attempt to identify region boundaries, coastline, or other features that are common to the images. The number of common elements of the detected sets of features should be sufficiently high, regardless of the change of image geometry, radiometric conditions and of changes in the scanned scenes. Nevertheless, the majority of the feature-based registration methods consist of the following four steps [12-14]:  
\(\bullet\) Feature detection: salient and distinctive objects (closed-boundary regions, capes, contours, _etc._) are manually or automatically detected. For further processing, these features can be represented by their point representatives (centers of gravity, line endings, distinctive points), which are called control points (CPs) in the literature. The detected feature sets in the reference and sensed images must have enough common elements, even in situations when the images do not cover exactly the same scene or when there are object occlusions or other unexpected changes. \(\bullet\) Feature matching. In this step, the correspondence between the features detected in the sensed image and those detected in the reference image is established. Various feature descriptors and similarity measures along with spatial relationships among the features are used for that purpose. \(\bullet\) Transform model estimation. The type and parameters of the so-called mapping functions, aligning the sensed image with the reference image, are estimated. The parameters of the mapping functions are computed by means of the established feature correspondence. \(\bullet\) Image resampling and transformation. The sensed image is transformed by means of the mapping functions. Image values in non-integer coordinates are computed by the appropriate interpolation technique. Aim at robust and reliable registration with high accuracy, in this paper we propose a new feature-based approach to automatic registration of remotely sensed images, according with the typical image registration procedure. Previously, to automatic satellite imagery registration, the reference and sensed images are geometrically corrected. For this task, a simple Kepplerian orbital model is considered as a reference model, and mean orbital elements are given as input to the model from ephemeris data. As shown in Figure 1, the task of the proposed image registration approach can be divided into three major components: reference image coastline extraction, sensed image gradient energy map estimation and feature correspondence by optimization of the reference image contours on the sensed image.