Automatic satellite streaks detection in astronomical images | ||
| فیزیک زمین و فضا | ||
| Article 2, Volume 43, Issue 3, October 2017, Pages 473-487 PDF (1.39 M) | ||
| DOI: 10.22059/jesphys.2017.61671 | ||
| Authors | ||
| saeed farzaneh* 1; Mohammad Ali Sharifi2; Mona Kosary3 | ||
| 1Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran | ||
| 2Associate Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran | ||
| 3M.Sc. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran | ||
| Abstract | ||
| 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. | ||
| Keywords | ||
| satellite tracking; satellite streak detection; MSAC algorithm; DBSCAN clustering algorithm | ||
| References | ||
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روغنی زاده، ا.، 2009، تعیین مدار ماهواره LEO با استفاده از امواج ارسالی از ماهواره. کارشناسی ارشد، دانشگاه صنعتی شریف. شریفی، م. ع.، فرزانه س. و سیف، م. 2015، تشخیص اتوماتیک ستارگان در یک سامانه نجومی بینایی مبنا برای ردیابی ماهوارهها.
Buades, A, Coll, B. and Morel, J. M. 2005, A review of image denoising algorithms, with a new one, Multiscale Modeling & Simulation 4(2), 490-530. Erdem, E., 2013 , NONLINEAR DIFFUSION. Ester, M., Kriegel, H.-P., Sander, J. and Xu, X., 1996, A density-based algorithm for discovering clusters in large spatial databases with noise. Fischler, M. A. and Bolles, R. C., 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM 24(6), 381-395. Gagliardi, R. M. and Karp, S., 1976, Optical communications, New York, Wiley-Interscience, 1976. 445 p. 1. Gerig, G., Kubler, O., Kikinis, R. and Jolesz, F. A., 1992, Nonlinear anisotropic filtering of MRI data, IEEE Transactions on medical imaging 11(2), 221-232. Gonzalez, R. C. and Woods, R. E., 2009, Digital Image Processing, Pearson Education. Hejduk, M., Lambert, J., Williams, C. and Lambour, R., 2004, Satellite detectability modeling for optical sensors, AMOS Technical Conference. Montenbruck, O. and Gill, E., 2012, Satellite orbits: models, methods and applications, Springer Science & Business Media. Stöveken, Schildknecht, T. 2005, Algorithms for the optical detection of space debris objects. Schildknecht, T. 1994,Optical astrometry of fast moving objects using CCD detectors, Geod.-Geophys. Arb. Schweiz, No. 49 49. P. Prona and Malik, J., 1990, Scale-Space and Edge Detection Using Anisotropic Diffusion, IEEE Transactions on pattern analysis and machine inteligence, vol (12), p 629-639 Torr, P. H. and Zisserman, A., 2000, MLESAC: A new robust estimator with application to estimating image geometry, Computer Vision and Image Understanding 78(1), 138-156. van der Wal, A. C., Becker, A., Van der Loos, C. and Das, P., 1994, Site of intimal rupture or erosion of thrombosed coronary atherosclerotic plaques is characterized by an inflammatory process irrespective of the dominant plaque morphology, Circulation 89(1), 44-36. Wang, Z., Bovik, A. C., Sheikh, H. R. and Simoncelli, E. P., 2004, Image quality assessment: from error visibility to structural similarity, IEEE transactions on image processing 13(4), 600-612. Weeratunga, S. K. and Kamath, C., 2002, PDE-based nonlinear diffusion techniques for denoising scientific and industrial images: an empirical study, Electronic Imaging 2002, International Society for Optics and Photonics. Weeratunga, S. K. and Kamath, C., 2003, Comparison of PDE-based non-linear anistropic diffusion techniques for image denoising. Electronic Imaging 2003, International Society for Optics and Photonics | ||
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