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Image geometrization is one of the basic processes in satellite image processing. As a result of the transformations performed, georeference is attached to the image becoming a cartometric image. Depending of the used algorithm, the referencing material can be a map, other image, a vectorial data base, control points interactively determined by an operator or RPC points (Rational Polynomial Coefficient). In everyday practice working with remote sensing means that we work more often with after orthorectification data, realized by image supplier. Despite this, “pixel to pixel” matching is still frequently needed. This is particularly important when we perform simultaneous classification of various images or comparing analyses, for example, detecting change. Image matching of satellite, aerial or other imaging data originated from scanning, is commonly hand made based on marked points by an operator. This is not a difficult process, however timeconsuming and often troublesome. Some of the commercial software applications offer functionalities that do this process automatically, but frequently appear in additional paid modules. At the Space Research Centre in Earth Observation Group we have developed an automated image matching method that works integrated in a created stand-alone software. Matching points at reference and input image are marked automatically. To this end, edge detection is performed on the image using Canny’s algorithm. After this, straight lines are identified and on the intersection points between these lines, characteristic image points are created. From these points both images will select corresponding pairs of points to be matched. The points selected for this task must fulfill three conditions. Firstly, maximal and minimal distance between the points must be kept within the defined threshold values. Secondly, the angle between intersected segments that define a matching point must be similar. And at lastly, the correlation coefficient indicating pixel value defined at the surrounding point zone must be the same, allowing a predetermined margin over the defined threshold value. Using the matching points obtained during this process, the parameters of the transformation matrix are obtained, being those parameters the base for geometric image correction. The purposed method is characterized by high accuracy of its results. The firsts tests were performed using Matlab development environment and then, taking in mind the increasing need of high speed performance, the algorithm was adapted to work using C\C++ libraries. Based on this algorithm, we have developed and implemented the software application matSIM. We have released this application under a freeware license and can be commonly used. The user friendly graphic interface improves the usability and facilitates image visualization and selection of used regions of interest where matching points will be searched. Additionally, the application allows changing default parameters such as transformation method used (lineal, bilinear, quadratic) and resampling type (nearest neighbor, bilinear). The input and output data format is GeoTIFF.
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