Energy Model of Optical System of Polarimetric Thermal Imager




polarimetric thermal imager, reflected and emitted radiation, Stokes vector, degree of polarization


Polarimetric remote sensing is a relatively new field of thermal imaging and has various fields of application. Recently, thermal imagers have been widely used in military affairs as monitoring systems that allow detecting and recognizing targets at the limit range during the day in difficult weather conditions, provided there is a thermal radiation contrast between the target and the background. In the absence of such contrast, the target can be detected with the help of the latest thermal imagers, in which the source of information is the difference in the polarization characteristics of the radiation of the target from the background (obstacles). The optical system and the matrix detector (MD) of the polarimetric thermal imager (PTI) allow to measure the parameters of the Stokes vector, on the basis of which the polarimetric image is formed.

The purpose of the article is to research the process of conversion of external and internal IR radiation in the PTI optical system to improve image quality. Two potential sources of polarized radiation are considered: the reflected energy, which is calculated by Fresnel's formulas, and the state of polarization of the emission radiation. The final state of polarization will depend on the degree of polarization caused by each of these components and their relative magnitude. The external and internal radiation arriving at the input pupil of the IR lens is taken into account, namely the flux reflected from the surface of the drone; own (emission) thermal radiation of the drone; atmospheric radiation between the target and PTI; reflected and intrinsic radiation of the surface of the phase plate and polarizer. Have been obtained formulas for calculating the parameters of the Stokes vector, as well as the degree of polarization of the radiation entering the MD pixel, which take into account the orientation of the polarizer, the degree of polarization of the radiation of the target and the atmosphere, the coefficients of radiation and reflection of the target, and the brightness of the atmosphere. The practical application of these formulas shows that the signal/noise ratio in PTI, and therefore the image quality and the maximum range of target discrimination, can be significantly improved compared to classic thermal imagers.

Author Biography

V. G. Kolobrodov, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

Doctor of Technical Sciences, Professor 



Schott J. R. (2009). Fundamentals of Polarimetric Remote Sensing. SPIE Press, 244 p. DOI:10.1117/3.817304.

Zhang J.-H., Zhang Y., Shi Z.-G. (2018). Enhancement of dim targets in a sea background based on long-wave infrared polarisation features. IET Image Processing, Vol. 12, Iss. 11, pp. 2042-2050. DOI: 10.1049/iet-ipr.2018.5607.

Zhang Y., Shi Z.-G., Qiu T. (2017). Infrared small target detection method based on decomposition of polarization information. Journal of Electronic Imaging, Vol. 26, Iss. 3, 033004. DOI:10.1117/1.JEI.26.3.033004.

Schuster N., Kolobrodov V. G. (2004). Infrarotthermographie: Zweite, Uberarbeitete Und Erweiterte Ausgabe [Infrared thermography. Second, revised and expanded edition]. WILEY-VCH: Berlin, Germany, 354 p.

Thermal imaging for military purposes., accessed 21 January 2024.

Vollmer M., Mollman K.-P. (2018). Infrared Thermal Imaging. Fundamentals, Research and Applications. Second Edition. WILEY-VCH, 788 p.

Goldstein D. H. (2011). Polarized Light. Third Edition. Taylor & Francis, CRC Press, 808 p. DOI:10.1201/b10436.

Russell Chipman, Wai-Sze Tiffany Lam, Garam Young. (2019). Polarized Light and Optical Systems. Taylor & Francis, CRC Press, 982 p. DOI: 10.1201/9781351129121.

Fei Liu, Xiaopeng Shao, Ying Gao et al. (2016). Polarization characteristics of objects in long-wave infrared range. Journal of the Optical Society of America A, Vol. 33, No. 2. pp. 237–243. DOI: 10.1364/JOSAA.33.000237.

Trongtirakul T., Agaian S., Oulefki A. and Panetta K. (2023). Method for Remote Sensing Oil Spill Applications Over Thermal and Polarimetric Imagery. IEEE Journal of Oceanic Engineering, Vol. 48, Iss. 3, pp. 973-987, doi: 10.1109/JOE.2023.3245759.

Driggers R. G., Friedman M. H., Devitt J. W., Furxhi O., Singh A. (2022). Introduction to Infrared and Electro-Optical Systems, Third Edition, Artech House, 712 p.

Kolobrodov, V. G., Mykytenko V. I., Tymchyk, G. S. (2020). Polarization model of thermal contrast observation objects. Journal of Thermoelectricity, no. 1, pp. 36–49.

Peri'c D., Livada B., Peri'c M. and Vuji'c S. (2019). Thermal Imager Range: Predictions, Expectations, and Reality. Sensors, Vol. 19, Iss. 15, 3313. DOI: 10.3390/s19153313.

Gurton K. P, Yuffa A. J., Videen G .W. (2014). Enhanced facial recognition for thermal imagery using polarimetric imaging. Optical Society of America, Vol. 39, No. 13. pp. 3857–3859. DOI: 10.1364/OL.39.003857.

Yang B., Wu T., Chen W., Li Y., Knjazihhin J., Asundi A., and Yan L. (2017). Polarization Remote Sensing Physical Mechanism, Key Methods and Application. The Intermati-on Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Wuhan, China, Vol. XLII-2/W7, pp. 955-960. doi: 10.5194/isprs-archives-XLII-2-W7-955-2017.



How to Cite

Колобродов, В. Г. (2024) “Energy Model of Optical System of Polarimetric Thermal Imager”, Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, (95), pp. 47-53. doi: 10.20535/RADAP.2024.95.47-53.



Computing methods in radio electronics

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