Automatic Satellite’s Streak Detection in Astronomical Images Based on Intelligent Methods

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

2 Associate Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

3 Ph.D. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

چکیده

The orbit determination in one sentence is the application of a variety of techniques for estimating the orbits of objects such as the moon, planets and spacecraft. In dynamic astronomy, the orbit determination is the process of determining orbital parameters with observations. Considering the visibility of the satellite motion trace and the fundamental need to determine and modify satellites’ orbital parameters as well as identify special satellites, determining the positional parameters of the satellite is also one of the modern and important applications of vision-based astronomical systems. In the modern vision-based astronomical systems, data collection is done using a charge-coupled device (CCD) array. In this paper, a new method is presented for satellite streak detection through an optical imaging system. This automatic and efficient method, which has the ability of real-time data analysis, is based on the sidereal image using CCDs. The images captured by this method have a large amount of information about stars, galaxy, and satellites’ streaks. In this paper, an automatic method is presented for streak detection. The purpose of this research is to find an optimal method for satellite streak detection and different methods in clustering such as k_means, particle swarm optimization (PSO), genetic algorithm (GA), and Gaussian mixture model (GMM). Finally, some assessment criteria were compared and concluded that GA is an optimal algorithm in satellite streak detection.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Automatic Satellite’s Streak Detection in Astronomical Images Based on Intelligent Methods

نویسندگان [English]

  • Saeed Farzaneh 1
  • Mohammad Ali Sharifi 2
  • Mona Kosary 3
1 Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran
2 Associate Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran
3 Ph.D. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran
چکیده [English]

The orbit determination in one sentence is the application of a variety of techniques for estimating the orbits of objects such as the moon, planets and spacecraft. In dynamic astronomy, the orbit determination is the process of determining orbital parameters with observations. Considering the visibility of the satellite motion trace and the fundamental need to determine and modify satellites’ orbital parameters as well as identify special satellites, determining the positional parameters of the satellite is also one of the modern and important applications of vision-based astronomical systems. In the modern vision-based astronomical systems, data collection is done using a charge-coupled device (CCD) array. In this paper, a new method is presented for satellite streak detection through an optical imaging system. This automatic and efficient method, which has the ability of real-time data analysis, is based on the sidereal image using CCDs. The images captured by this method have a large amount of information about stars, galaxy, and satellites’ streaks. In this paper, an automatic method is presented for streak detection. The purpose of this research is to find an optimal method for satellite streak detection and different methods in clustering such as k_means, particle swarm optimization (PSO), genetic algorithm (GA), and Gaussian mixture model (GMM). Finally, some assessment criteria were compared and concluded that GA is an optimal algorithm in satellite streak detection.

کلیدواژه‌ها [English]

  • Satellite tracking
  • Satellite streak detection
  • MSAC
  • Clustering
  • Swarm intelligence
Arias-Castro, E. and David, L.D., 2009, Does median filtering truly preserve edges better than linear filtering?. The Annals of Statistics, 37, 1172-206.
Buades, A., Bartomeu, C. and Jean-Michel, M., 2005, A review of image denoising algorithms, with a new one.  Multiscale Modeling & Simulation, 4, p. 490-530.
Coley, D.A., 1999, An introduction to genetic algorithms for scientists and engineers (World Scientific Publishing Company).
Eberhart, R. and James, K., 1995, A new optimizer using particle swarm theory. In Micro Machine and Human Science.
Esmin, A.A.A, Dilson, L.P. and FPA, D.A., 2008, Study of different approach to clustering data by using the particle swarm optimization algorithm. In Evolutionary Computation.
Farnocchia, D., Giacom, T., Andre, M. and Alessandro, R., 2010, Innovative methods of correlation and orbit determination for space debris. Celestial Mechanics and Dynamical Astronomy, 107, p. 169-85.
Fischler, M.A. and Robert, C.B., 1981, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24. 381-95.
Gerig, G., Olaf, K., Ron, K. and Feren, A.J., 1992, Nonlinear anisotropic filtering of MRI data. IEEE Transactions on medical imaging, 11, 221-32.
Gonzalez, R.C., 2009, Digital image processing: Pearson education india.
Grira, N., Michel, C. and Nozha, B., 2004, Unsupervised and semi-supervised clustering: a brief survey. A review of machine learning techniques for processing multimedia content, Report of the MUSCLE European Network of Excellence (FP6): 1001-30.
Hejduk, M.D, Lambert, J.V., Williams, C.M.  and Lambour, R.L., 2004, Satellite detectability modeling for optical sensors. In AMOS Technical Conference.
Jain, A.K., 2010, Data clustering: 50 years beyond K-means. Pattern recognition letters, 31, 651-66.
Kirchmaier, U., Simon, H. and Klaus, D., 2010, A line detection and description algorithm based on swarm intelligence. In Image Processing: Machine Vision Applications III, 75380Q.
Lee, D.J., 2003, kyung-Hee University.
Lee, W., Hyung-Chul, L., Pil-Ho, P., Jae-Hyuk, Y., Hong-Suh, Y. and Hong-Kyu, M., 2004, Orbit determination of GPS and KOREASAT 2 satellite using angle-only data and requirements for optical tracking system. Journal of Astronomy and Space Sciences, 21, 221-32.
Levesque, M., 2009, Automatic reacquisition of satellite positions by detecting their expected streaks in astronomical images. In Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, E81.
Levesque, M. P. and Sylvie, B., 2007, Image processing technique for automatic detection of satellite streaks. In.: DTIC Document.
Lim, C. P. and Satchidananda, D., 2009, Innovations in swarm intelligence. Springer Science & Business Media, 248.
Lowe, D.G., 1999, Object recognition from local scale-invariant features. The proceedings of the seventh IEEE international conference on, 1150-57.
Said, A.B, Rachid, H., Kamal, E.M. and Sebti, F., 2016, Multispectral image denoising with optimized vector non-local mean filter. Digital Signal Processing, 58, 115-26.
Schildknecht, T., 1994, Optical astrometry of fast moving objects using CCD detectors. Geod.-Geophys. Arb. Schweiz, 49.
Schildknecht, T., 2007, Optical surveys for space debris. The Astronomy and Astrophysics Review, 14, 41-111.
Scrucca, L., 2013, GA: a package for genetic algorithms in R. Journal of Statistical Software, 53, 1-37.
Simms, L.M., 2011, Autonomous subpixel satellite track end point determination for space-based images. Applied optics, 50, p. D1-D6.
Suganthan, P.N., 1999, Particle swarm optimiser with neighbourhood operator. Evolutionary Computation., 3.
Suzuki, K., Isao, H. and Noboru, S., 2003, Linear-time connected-component labeling based on sequential local operations, Computer Vision and Image Understanding, 89, 1-23.
Swanzy, M., 2007, Analysis and demonstration: a proof-of-concept compass star tracker, Texas A&M University.
Vallado, D. and Vladimir, A., 2010, Orbit Determination Results for Optical Measurements. In AIAA/AAS Astrodynamics Specialist Conference, 7525.
Van der Merwe, D.W. and Engelbrecht, A.P., 2003, Data clustering using particle swarm optimization. In Evolutionary Computation, 2003. CEC'03, 215-20.
Wang, Y., 2017, 'Reliability Analysis of Spintronic Device Based Logic and Memory Circuits', Télécom ParisTech.
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, p. 600-12.
Weeratunga, S.K. and Chandrika, K., 2002, PDE-based nonlinear diffusion techniques for denoising scientific and industrial images: an empirical study. International Society for Optics and Photonics, 279-90.
Weeratunga, S.K. and Kamath, C., 2003, Comparison of PDE-based non-linear anistropic diffusion techniques for image denoising. In Image Processing: Algorithms and Systems II, 201-13. International Society for Optics and Photonics.
Zivkovic, Z., 2004, Improved adaptive Gaussian mixture model for background subtraction. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 28-31. IEEE.