The distortion of images in remote sensing systems at arbitrary angles of sight
|1Kolobrodov, VG, 2Lykholit, NI, 2Tiagur, VM, 2Pinchuk, BYu., 1Lutsiuk, MM |
1National Technical University of Ukraine «Kyiv Polytechnic Institute», Kyiv, Ukraine
2Special Device Production State Enterprise “Arsenal”, Kyiv, Ukraine
|Space Sci. & Technol. 2021, 27 ;(3):51-65|
|Publication Language: Ukrainian|
Background. The main problem in launching space optical and electronic viewing systems (OEVS) for remote sensing of the Earth can be regarded as their high price, which even the leading countries of the world are not always ready to pay. Therefore, the quality of spacecraft systems imposed the most stringent requirements. One of the economically expedient options to increase the efficiency of space OEVS is scanning the Earth’s surface at arbitrary angles of sighting, which allows for the same time of service life to collect more information, but this in turn leads to image distortion. Therefore, analysis of the resulting image quality depending on the angles of sighting of the OEVS is an actual task that will assess the capabilities of the system and its conformance with the established requirements.
Objective. Improving the physical and mathematical model of the modulation transfer function of the system “lens – matrix detector” and the study of the dependence of spatial and radiometric resolution on the angles of sight for the space OEVS when the sighting axis deviates from the nadir.
Methods. Based on the analysis of signal generation models for television and thermal imaging space OEVS, it is proposed to use the concept – the contrast gray body. In the physical and mathematical model, it is proposed normalize to the spatial frequencies of objects at different angles of sight to the spatial frequencies in the nadir, and to calculate the radiometric resolution take into account the transmission and rarefied of the atmosphere, the image movement speed on the detector and its integration time.
Results. Practical results of calculations of the offered physical and mathematical model for space OEVS showed that at deviation from nadir the effective spatial bandwidth worsens and at the specified parameters of system it is inexpedient scanning at angles of sighting greater than 30º. Accordingly, a comparative analysis of radiometric resolution for different type of detectors showed that the use of a photonic detector gives ~1.4 times better resolution in the nadir as opposed to the use of thermal detector and almost identical results are obtained at maximum angles of sighting. Also, a significant impact is made by a decrease a coefficient of atmospheric transmittance due to the rarefied of the atmosphere, which reaches from 26% to 45% that depends on the spectral range.
Conclusions. Analysis of the results of the study confirms the possibility that photonic detectors can be replaced by modern thermal detectors with insignificant loss of image quality of the resulting image, which can significantly increase the service life of space OEVS.
|Keywords: deviations in the angles of sighting, image distortion, modulation transfer function, optical and electronic viewing systems, radiometric resolution, remote sensing of the Earth|
1. Kolobrodov V. G., Liholit N. I. (2007). Development of thermovision and television systems. Kyiv: NTUU “KPI”.
2. Kolobrodov V. G., Tiagur V. M., Pinchuk B. Yu. (2018). Modulation transfer function of the remote sensing system when the line of sight deviates from the nadir. KPI Sci. News, No 6, 66—80.
3. Kolobrodov V. G. (2015). Parameters Optimization of the Imager’s Lens and Microbolometer Matrix. KPI Sci. News, No 1(99), 91—95.
4. Kolobrodov V. G., Tiagur V. M., Pinchuk B. Yu. (2018). Influence of angles of sighting and the Earth’s surface curvature on the spatial resolution of the space electro-optical viewing system. KPI Sci. News, No 5, 63—75.
5. Jamieson J. A., McFee R. H., Plass G. N., Grube R. H., Richards R. G. (1963). Infrared Physics and Engineering. New York: McGraw-Hill.
6. Kriksunov L. Z. (1978). Handbook on the basics of infrared technology. Moscow: Sovetskoe radio.
7. Patrashin A. I. (2010). Calculation technique of IR array photoelectric parameters. Orion Research-and-Production Associa-tion, No 2, 103—108.
8. Hudson R. D. (1969). Infrared System Engineering. New York: John Wiley and Sons Ltd.
9. Boreman G. D. (2001). Modulation transfer function in optical and electro-optical systems. Bellingham, Washington USA: SPIE Press.
10. Campbell J. B., Wynne R. H. (2011). Introduction to Remote Sensing. New York, London: The Guilford Press.
11. Capderou M. (2005). Satellites. Orbits and Missions. Paris France: Springer-Verlag France.
12. Capderou M. (2014). Handbook of Satellite Orbits. From Kepler to GPS. Paris France: Springer International Publishing Switzerland.
13. Chen C. H. (2012). Signal and Image Processing for Remote Sensing. Boca Raton, New York, London: CRC Press.
14. Deng Q., Feng H., Xu Z., Li Q., Chen Y., Dong W. (2012) Study on MTF of remote sensing imaging under arbitrary known vibration. Proc. of SPIE, No 8420, 84200W-1-84200W-6.
15. Dowman I., Jacobsen K., Konecny G., Sandau R. (2012). High Resolution Optical Satellite Imagery. Dunbeath, UK: Whit-tles Publ.
16. Driggers R. G., Friedman M. H., Nichols J. M. (2012). Introduction to Infrared and Electro-Optical Systems. Boston, Lon-don: Artech House.
17. Driggers R. G., Young S. S., Jacobs E. L. (2008). Signal Processing and Performance Analysis for Imaging Systems. Boston, London: Artech House.
18. Fiete R. D. (2010). Modeling the Imaging Chain of Digital Cameras. Bellingham, Washington USA: SPIE Press.
19. Kolobrodov V. G., Lykholit M. I., Mykytenko V. I., Tiagur V. M., Dobrovolska K. V. (2017). Calculation Model for Op-toelectronic Remote Sensing System’s Radiometric Resolution at Arbitrary Viewing Angles. Visnyk NTUU KPI.Ser. Radiotekhnika Radioaparatobuduvannia, No 69, 30—34.
20. Kolobrodov V. G., Mykytenko V. I., Tymchik G. S., Tiagur V. M., Dobrovolska C. V., Komada P., Mussabekova A., Targeu-sizova A., Iskakova A. (2017). Spaceborne linear array imager’s spatial resolution for arbitrary viewing angles. Proc. of SPIE, No 10445, 104450J-1—104450J-9.
21. Li J., Liu Z., Liu F. (2017). Using sub-resolution features for self-compensation of the modulation transfer function in re-mote sensing. Opt. Soc. Amer., 4018—4037.
22. Rainer S. (2010). Digital Airborne Camera. Introduction and Technology. Dordrecht Netherlands: Springer.
23. Reda I., Andreas A. (2008). Solar Position Algorithm for Solar Radiation Applications. Golden, Colorado: National Renew-able Energy Laboratory.
24. Rogalski A. (2019). Infrared and Terahertz Detectors. Boca Raton, New York, London: CRC Press
25. Shen-En Q. (2013). Optical Satellite Signal Processing and Enhancement. Bellingham, Washington USA: SPIE Press.
26. Vollmerhausen R. H., Reago D. A. Jr., Driggers R. G. (2010). Analysis and evaluation of sampled imaging systems. Belling-ham, Washington USA: SPIE Press.
27. Weng Q. (2011). Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications. Boca Raton, New York, London: CRC Press.
28. Williams Dr. D. R. (2004). Sun Fact Sheet. NASA Goddard Space Flight Center. USA. URL: https://web.archive.org/web/20100715200549/http://nssdc.gsfc.nasa.gov/planetary/factsheet/sunfact.html. (Last accessed: 24.02.2021).
29. Williams Dr. D. R. (2018). Sun Fact Sheet. NASA Goddard Space Flight Center. USA. URL: https://nssdc.gsfc.nasa.gov/planetary/factsheet/sunfact.html. (Last accessed: 24.02.2021).
30. Xin M., Shuyan X., Guangxin L., Jun H. (2012). Remote Sensing Image Restoration with Modulation Transfer Function Compensation Technology In-orbit. Proc. of SPIE, No8768, 87681K-1—87681K-5.