عنوان مقاله [English]
In the current state of colonization of near Earth space by satellites, there is an increasing need to know exactly the real status of occupation of this space. Thus, orbital parameters for all objects travelling in this space must be known with a high degree of accuracy, and this knowledge must be periodically updated, because this situation is always changing. Atmospheric drag, solar wind, moon and planetary gravitational perturbations, Earth oblateness, etc. are all sources of interference that generate orbital perturbations beyond what the best orbital model can predict. The solution is to periodically observe all the satellites, particularly the debris (because active satellites themselves contribute to maintain the knowledge of their orbital parameters), determine with precision their positions and update their known orbital parameters. There is a real need for sky surveillance in order to monitor either the satellites or the non-functional space objects for different purposes, such as to correct the satellites deviations from their trajectories, to detect uncataloged space debris objects and to avoid possible collisions. In order to define the location of the satellite in the sky and then to update its orbital parameters, an optical satellite tracking system can be designed which acquires sequences astronomical images from the sky. Such system is composed of many sensors like a telescope, a CCD camera, a GPS receiver, etc. Also, some reference data such as the star catalogues and the Two Lines Element (TLE) database are used. The telescope is used to search the sky and point to the satellite, precisely. The CCD camera acquires some sequences images in a current time provided by GPS. The star catalogues are employed to calibrate the image plane to the celestial coordinate systems. The TLE database contains the out-dated orbital parameters to estimate the satellite position. For this purpose an algorithms and software that can automatically detect and report the presence of satellite streaks in the acquired images are needed. The algorithms presented in this document were developed for this purpose. The image processing technique presented in this document is a collection of algorithms used to detect and classify everything that can be observed in the image, such as stars, satellite streaks and image artefacts. First due to the use of digital imagery, the quality of digital images is critical and affects the final product. Different noises in imaging phase could degrade the quality of image, for this purpose the non-linear diffusion filter has been used. This technique, is based on the use of partial differential equations, the idea behind the use of the diffusion equation in image processing arose from the use of the Gaussian filter in multi-scale image analysis. Second for the removal of the image background the stars have been detected using SIFT method. In this method the star's centers are extracted with sub-pixel precision, then they have been subtracted from image in an iteration producer. Third the clustering method has been applied for satellite streak detection. In this way the Density-based spatial clustering of applications with noise (DBSCAN) which is a density-based clustering algorithm has been used, finally MSAC algorithm has been implemented for streak model extraction.