###### Abstract  
This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pleiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles. automatic image processing; geometric corrections; ground control points; very-high resolution; rational function model; urban areas +
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image registration methods have been developed in recent times (e.g., [2; 3; 4; 5; 6]). These methods are commonly composed by two important processes: ground control points (GCPs) extraction and geometric transformation (modelling). Automatic GCP extraction is done by image matching methods. These methods can be roughly divided into two groups: area-based and feature-based [7]; within the remote sensing domain, the two approaches are often combined at different levels [8]. These methods that are image intensity-based usually require that the target satellite image be compared to an already georeferenced reference image. The correspondence is usually done using local image features, such as SIFT (Scale-Invariant Feature Transform) [9], but problems may arise when matching multi-temporal or multi-sensor images [10]. Some of the feature-based methods extract GCPs with an object-level processing approach. In these methods, objects are extracted from the input image and are matched to reference digital data. The road network is taken in most circumstances for the reference digital data, since it is a stable structure that lies on the ground (Earth's surface) and is easily visually recognised. The reference roads can be found in national topographic databases or web databases (e.g., OpenStreetMap). Although several studies addressed this topic (e.g., [11]), it has rarely been extensively studied and utilized in practical applications [12]. Newer matching methods also work on more complex descriptors (e.g., [13]. The road extraction from a VHR image, which is also used in our solution, is a challenging issue and is very important for urban design and planning, navigation, traffic monitoring, and image registration in general [14]. The topic of road extraction is of great significance and many algorithms were proposed in the last decades. [PERSON] [15] already reviewed the techniques of extracting linear features from imagery that can be used also for road extraction. [PERSON] et al. conducted a more recent review of road extraction from remote sensing [16]. Among the road detection methods, the region-based techniques [17] are widely used and they can effectively extract linear features with segmentation and classification [18; 19]. Due to the wide variations of road types (e.g., city, rural) and the occlusion problems due to buildings, shadows, trees, vehicles, and other objects, these kind of techniques have several problems and often fail when dealing with complex road networks [17]. For urban areas, some of the mentioned problems can be surpassed with applying graph-theory onto extracted features [20].