Advertisement

Satellite Dual-Polarization Radar Imagery Superresolution Under Physical Constraints

  • Sergey StankevichEmail author
  • Iryna Piestova
  • Sergey Shklyar
  • Artur Lysenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

Abstract

A novel physically justified method for spatial resolution enhancement of satellite dual-polarization synthetic aperture radar data is proposed. The method starts from the conversion of the specific land surface radar backscattering into the land surface dielectric permittivity in each polarization band separately. Said conversion is founded on a well-known integral equation model of synthetic aperture radar (SAR) backscattering. Transition from raw radar data to dielectric permittivity forms a common unified image field in each polarization band. Due to the SAR platform’s own movement, these fields are affected by some subpixel shift to each other. So, the opportunity to apply the superresolution technique over all permittivity fields at once is enabled with considering ones’ subpixel shift. Dual-image iterative superresolution based on Gaussian regularization was used. A standalone software module for statistical estimation of inter-images subpixel shift was developed earlier and applied in current research. A noticeable spatial resolution enhancement of the land surface dielectric permittivity field was achieved. This was demonstrated and quantified for actual dual-polarization radar images from the Sentinel-1 European SAR satellite system over two test sites in Ukraine.

Keywords

SAR imagery Satellite dual-polarization radar Superresolution Subpixel shift Physical constraints Land surface dielectric permittivity Land cover types 

References

  1. 1.
    Zatyagalova, V.V., Ivanov, A.Y., Golubov, B.N.: Application of ENVISAT SAR imagery for mapping and estimation of natural oil seeps in the south Caspian sea. In: Proceedings of the ‘ENVISAT Symposium 2007’, Montreux, pp. 1–6. ESA (2007)Google Scholar
  2. 2.
    Kusky, T.M., Ramadan, T.M.: Structural controls on Neoproterozoic mineralization in the South Eastern Desert, Egypt: an integrated field, Landsat TM, and SIR-C/X SAR approach. J. Afr. Earth Sci. 35, 107–121 (2002)CrossRefGoogle Scholar
  3. 3.
    Ramadan, T.M.: Use of ERS-2 SAR and Landsat TM images for geological mapping and mineral exploration of Sol Hamid Area, South Eastern Desert, Egypt. Egypt. J. Remote Sens. Space Sci. VI, 13–24 (2003)Google Scholar
  4. 4.
    Amitrano, D., Di Martino, G., Iodice, A., Mitidieri, F., Papa, M.N., Riccio, D., Ruello, G.: Sentinel-1 for monitoring reservoirs: a performance analysis. Remote Sens. 6(11), 10676–10693 (2014)CrossRefGoogle Scholar
  5. 5.
    Gao, Q., Zribi, M., Escorihuela, M.J., Baghdadi, N.: Synergetic use of Sentinel-1 and Sentinel-2 data for soil moisture mapping at 100 m resolution. Sensors 17(9), 21 (2017). a.1966Google Scholar
  6. 6.
    Stankevich, S.A., Kozlova, A.A., Piestova, I.O., Lubskyi, M.S.: Leaf area index estimation of forest using Sentinel-1 C-band SAR data. In: Proceedings of 5th Microwaves, Radar and Remote Sensing Symposium (MRRS 2017), Kiev, pp. 253–257. IEEE (2017)Google Scholar
  7. 7.
    Ulaby, F., Long, D.G.: Microwave Radar and Radiometric Remote Sensing. University of Michigan Press, Ann Arbor (2013)Google Scholar
  8. 8.
    Geudtner, D., Torres, R., Snoeij, P., Ostergaard, A., Navas-Traver, I., Rommen, B., Brown, M.: Sentinel-1 system overview and performance. In: Proceedings of ‘ESA Living Planet Symposium 2013’, Edinburgh, 4 p. ESA (2013)Google Scholar
  9. 9.
    Stankevich, S.A., Shklyar, S.V., Podorvan, V.N., Lubskyi, N.S.: Thermal infrared imagery informativity enhancement using sub-pixel co-registration. In: International Conference on Information and Digital Technologies (IDT), Rzeszów, pp. 245–248. IEEE (2016)Google Scholar
  10. 10.
    Piestova, I., Lubskyi, M., Svideniuk, M., Golubov, S., Sedlacek, P.: Satellite imagery resolution enhancement for urban area thermal micromapping. Cent. Eur. Res. J. 4(1), 35–39 (2018)Google Scholar
  11. 11.
    Zaitseva, E., Piestova, I., Rabcan, J., Rusnak, P.: Multiple-valued and fuzzy logics application to remote sensing data analysis. In: 26th Telecommunications Forum (TELFOR), Belgrade, pp. 1–4. IEEE (2018)Google Scholar
  12. 12.
    Stankevich, S.A., Lubskyi, M.S., Mosov, S.P.: Natural color aerial imagery superresolution with bands radiometric conversion. In: Proceedings of 17th International Conference on Mathematical Methods in Electromagnetic Theory (MMET 2018), Kiev, pp. 99–102. IEEE (2018)Google Scholar
  13. 13.
    Brodu, N.: Super-resolving multiresolution images with band-independent geometry of multispectral pixels. IEEE Trans. Geosci. Remote Sens. 55(8), 4610–4617 (2017)CrossRefGoogle Scholar
  14. 14.
    Zhu, H., Tang, X., Xie, J., Song, W., Mo, F., Gao, X.: Spatio-temporal super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement. Sensors 18(2), 20 (2018). a. 498Google Scholar
  15. 15.
    Zhu, L., Suomalainen, J., Liu, J., Hyyppä, J., Kaartinen, H., Haggren, H.: A review: remote sensing sensors. In: Rustamov, R.B., Hasanova, S., Zeynalova, M.H. (eds.) Multi-purposeful Application of Geospatial Data, pp. 19–42. IntechOpen, London (2018)Google Scholar
  16. 16.
    Karybali, I.G., Psarakis, E.Z., Berberidis, K., Evangelidis, G.D.: An efficient spatial domain technique for subpixel image registration. Signal Process. Image Commun. 23(9), 711–724 (2008)CrossRefGoogle Scholar
  17. 17.
    Ben-Ezra, M., Zomet, A., Nayar, S.K.: Video super-resolution using controlled subpixel detector shifts. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 977–987 (2005)CrossRefGoogle Scholar
  18. 18.
    Belenok, V.Yu., Burachek, V.G., Zatserkovny, V.I., Popov, M.A., Stankevich, S.A.: Subpixel image acquisition for detailed aerospace imaging. In: Proceedings of the 8th International Conference on Digital Technologies (DT 2011), pp. 190–193. University of Žilina, Žilina (2011)Google Scholar
  19. 19.
    Stankevich, S.A., Shklyar, S.V., Tiagur, V.M.: Subpixel resolution satellite imaging technique. In: Proceedings of the 9th International Conference on Digital Technologies (DT 2013), pp. 81–84. University of Žilina, Žilina (2013)Google Scholar
  20. 20.
    Park, S.C., Park, M.K., Kang, M.G.: Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag. 20(3), 21–36 (2003)CrossRefGoogle Scholar
  21. 21.
    Young, S.S., Driggers, R.G., Jacobs, E.L.: Signal Processing and Performance Analysis for Imaging Systems. Artech House, Norwood (2008)zbMATHGoogle Scholar
  22. 22.
    Stone, H.S., Orchard, M.T., Chang, E.-C., Martucci, S.A.: A fast direct Fourier-based algorithm for subpixel registration of images. IEEE Trans. Geosci. Remote Sens. 39(10), 2235–2243 (2001)CrossRefGoogle Scholar
  23. 23.
    Vandewalle, P., Baboulaz, L., Dragotti, P.L., Vetterli, M.: Subspace-based methods for image registration and super-resolution. In: Proceedings of the 15th International Conference on Image Processing (ICIP 2008), San Diego, pp. 645–648. IEEE (2008)Google Scholar
  24. 24.
    Shekarforoush, H., Berthod, M., Zerubia, J.: Subpixel image registration by estimating the polyphase decomposition of cross-power spectrum. In: Proceedings of the Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 1996), San Francisco, pp. 532–537. IEEE (1996)Google Scholar
  25. 25.
    Gerchberg, R.: Super-resolution through error energy reduction. Acta Opt. 21(9), 709–720 (1974)CrossRefGoogle Scholar
  26. 26.
    Papoulis, A.: A new algorithm in spectral analysis and band-limited extrapolation. IEEE Trans. Circ. Syst. 22(9), 735–742 (1979)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Stark, H., Oskoui, P.: High-resolution image recovery from image-plane arrays using convex projections. J. Opt. Soc. Am. 7(11), 1715–1726 (1989)CrossRefGoogle Scholar
  28. 28.
    Ayers, G.R., Dainty, J.C.: Iterative blind deconvolution method and its applications. Opt. Lett. 13(7), 547–549 (1988)CrossRefGoogle Scholar
  29. 29.
    Popov, M.A., Stankevich, S.A.: About restoration of the scanning images received onboard a Sich-1M space vehicle by inverse filtering method. In: Proceedings of the 31st International Symposium on Remote Sensing of Environment, Saint Petersburg, pp. 488–490. ISPRS (2005)Google Scholar
  30. 30.
    Nguyen, N., Milanfar, P.: A wavelet-based interpolation-restoration method for superresolution (wavelet superresolution). Circ. Syst. Signal Process. 18(4), 321–338 (2000)CrossRefGoogle Scholar
  31. 31.
    Lyalko, V.I., Popov, M.A., Stankevich, S.A., Shklyar, S.V., Podorvan, V.N., Likholit, N.I., Tyagur, V.M., Dobrovolska, C.V.: Prototype of satellite infrared spectroradiometer with superresolution. J. Inf. Control Manag. Syst. 12(2), 153–164 (2014)Google Scholar
  32. 32.
    Ulaby, F.T., Moore, R.K., Fung, A.K.: Microwave Remote Sensing: Active and Passive, vol. II. Artech House, Dedham (1982)Google Scholar
  33. 33.
    Ku, C.-S., Chen, K.-S., Chang, P.-C., Chang, Y.-L.: Imaging simulation for synthetic aperture radar: a full-wave approach. Remote Sens. 10(9), 16 (2018). a. 1404CrossRefGoogle Scholar
  34. 34.
    Wang, H.: Soil moisture retrieval from microwave remote sensing observations. In: Civeira, G. (ed.) Soil Moisture, pp. 29–54. IntechOpen, London (2019)Google Scholar
  35. 35.
    Freeman, A.: Radiometric calibration of SAR image data. ISPRS Arch. XXIX(B1), 212–222 (1992)Google Scholar
  36. 36.
    Kang, W., Yu, S., Ko, S., Paik, J.: Multisensor superresolution using directionally adaptive regularization for UAV images. In: Toro, F.G., Tsourdos, A. (eds.) UAV Sensors for Environmental Monitoring, pp. 272–294. MDPI, Basel (2018)Google Scholar
  37. 37.
    Soares, J.V., Rennó, C.D.: Soil moisture retrieval from active microwave remote sensing. In: Proceedings of the First Latino-American Seminar on Radar Remote Sensing – Image Processing Techniques, Buenos Aires, pp. 192–203. ESA (1997)Google Scholar
  38. 38.
    Stankevich, S.A., Shklyar, S.V., Lysenko, A.R.: Software module for estimating subpixel shift of images acquired from quadcopter. Ukr. J. Remote. Sens. 17, 10–13 (2018)Google Scholar
  39. 39.
    Kennedy, R.E., Cohen, W.B.: Automated designation of tie-points for image-to-image coregistration. Int. J. Remote Sens. 24(17), 3467–3490 (2003)CrossRefGoogle Scholar
  40. 40.
    Popov, M.O., Stankevich, S.A., Shklyar, S.V.: An algorithm for resolution enhancement of subpixel displaced images. Math. Mach. Syst. 1, 29–36 (2015)Google Scholar
  41. 41.
    Liu, H.Y., Zhang, Y.S., Ji, S.: Study on the methods of super-resolution image reconstruction. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. XXXVII(B2), 461–466 (2008)CrossRefGoogle Scholar
  42. 42.
    Chen, C.H.: Signal and Image Processing for Remote Sensing. CRC Press, Boca Raton (2012)CrossRefGoogle Scholar
  43. 43.
    Stankevich, S., Piestova, I., Shklyar, S., Lysenko, A.: Satellite dual-polarization radar imagery superresolution under physical constraints. In: Proceedings of the International Scientific Conference “Computer Sciences and Information Technologies” (CSIT 2019), vol. 3, pp. 228–231 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Scientific Centre for Aerospace Research of the Earth, National Academy of Sciences of UkraineKievUkraine

Personalised recommendations