Algorithm for eliminating gas absorption effects on hyperspectral remote sensing data
Nikolaeva O.V.

Keldysh Institute of Applied Mathematics RAS, Moscow, Russia

 PDF

Abstract:
An efficient algorithm for the elimination of gas absorption effects on the reflectance of sunlight in an atmosphere-ground system is proposed. The algorithm does not require aerosol, surface and gas concentration information. The corrected reflectance is obtained via the correction factor, which is found via analysis of the spectral dependence of reflectance. The algorithm is applicable only to hyperspectral data. Results of testing on model problems are presented.

Keywords:
atmospheric correction, reflectance, gas absorption effects.

Citation:
Nikolaeva, OV Algorithm for eliminating gas absorption effects on hyperspectral remote sensing data. Computer optics. 2018; 42(2): 328-337. DOI: 10.18287/2412-6179-2018-42-2-328-337.

References:

  1. Tarasenkov MV, Belov VV. Software package for reconstruction of reflective properties of the Earth surface in visible and UV ranges. Atmospheric and Oceanic Optics 2014; 28(1): 89-94. DOI: 10.1134/S1024856015010133.
  2. Shanmugam P. CAAS: an atmospheric correction algorithm for the remote sensing of complex water. Ann Geophys 2012; 30(1): 203-220. DOI: 10.5194/angeo-30-203-2012.
  3. Gao BC, Montes MJ, Davis CO, Goetz AFH. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens Env 2009; 113(1): S17-S24. DOI: 10.1016/j.rse.2007.12.015.
  4. Minu S Shetty A.Atmospheric correction algorithms for hyperspectral imageries: A review. Int Res J Earth Sci 2015; 3(5): 14-18.
  5. Lenot X, Achard V, Poutier L. SIERRA: A new approach to atmospheric and topographic corrections for hyperspectral imagery. Remote Sen Env 2009; 113(8): 1664-1677. DOI: 10.1016/j.rse.2009.03.016.
  6. Hadjit H, Oukebdane A, Belbachir AH.Atmospheric correction of Earth-observation remote sensing images by Monte Carlo method. J Earth Syst Sci 2013; 122(5): 1219-1235. DOI: 10.1007/s12040-013-0337-4.
  7. Katkovsky LV. Parameterization of outgoing radiation for quick atmospheric correction of hyperspectral images [In Russian]. Atmospheric and Oceanic Optics 2016; 29(9): 778-784. DOI: 10.15372/AOO20160909.
  8. Griffin MK, Burke HK. Compensation of Hyperspectral Data for Atmospheric Effects. Lincoln Laboratory Journal 2003; 14(1): 29-54.
  9. Thompson DR, Gao BC, Green RO, Roberts DA, Dennison PE, Lundeen SR.Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign. Remote Sens Environ 2015; 167: 64-77. DOI: 10.1016/j.rse.2015.02.010.
  10. Qu Z, Kindel B, Goetz AFH. The High Accuracy Atmospheric Correction for Hyperspectral Data (HATCH) model. IEEE Trans Geosci Remote Sens 2003; 41(6): 1223-1231. DOI: 10.1109/TGRS.2003.813125.
  11. Marion R, Michel R, Faye Ch. Atmospheric correction of hyperspectral data over dark surfaces via simulated annealing. Proc of SPIE 2005; 5979: 59791T. DOI: 10.1117/12.622685.
  12. Derkacheva АА, Tutubalina OV. The effectiveness of atmospheric correction for Hyperion hyperspectral images in regions with developed vegetation cover. Current problems in remote sensing of the earth from space 2014; 11(4): 360-368.
  13. Denisova AY, Juravel YN, Myasnikov VV. Estimation of parameters of a linear spectral mixture for hyperspectral images with atmospheric distortions. Computer Optics 2016; 40(3): 380-387. DOI: 10.18287/2412-6179-2016-40-3-380-387.
  14. Bukanova T, Vazyulya S, Kopelevich O, Burenkov V, Sheberstov S, Aleksandrov S. Development of regional algorithms for the atmospheric correction of satellite ocean color data in the South-Eastern Baltic. Current problems in remote sensing of the earth from space 2012; 9(4): 70-79.
  15. GOST R 52870-2007. Joint use information displaying means. Requirement to visual representation of information and measurement means [In Russian]. Moscow: “Standartinform” Publisher; 2008.
  16. Rozanov VV, Rozanov AV. Differential optical absorption spectroscopy (DOAS) and air mass factor concept for a multiply scattering vertically inhomogeneous medium: theoretical consideration. Atmos Meas Tech 2010; 3: 751-780. DOI: 10.5194/amt-3-751-2010.
  17. Sayer AM, Smirnov A, Hsu NC, Holben BN. A pure marine aerosol model, for use in remote sensing applications. J Geophys Res 2012; 117: D05213. DOI: 10.1029/2011JD016689.
  18. GOST 4401-81. Standard atmosphere. Parameters [In Russian]. Moscow: “IPK Izdateljstvo standartov” Publisher; 2004.
  19. Gertsev MN. Reconstruction of molecular absorption cross-sections of radiation from database HITRAN [In Russian]. Keldysh Institute Preprints 2016; 19: 1-22. DOI: 10.20948/prepr-2016-19.
  20. Baula GG, Brychikhin MN, Istomina MI, Krotkov AYu, Szhyonov EYu, Rizvanov AA, Tret’yakov VN. Development of a database of hyperspectral optical characteristics of agricultural crops in the ultraviolet, visible and near infrared regions’ spectrum. Cosmonautics and Rocket Engineering 2013; 4(73): 178-184.
  21. Rizvanov A.A. Hyperspectral observations of the atmosphere – Earth’s surface system in the ultraviolet, visible and infrared spectral region from the international space station. Cosmonautics and Rocket Engineering 2015; 6(85): 39-44.
  22. Germogenova TA. Local properties of solutions on thransport equation [In Russian]. Moscow: “Nauka” Publisher; 1986.

© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: journal@computeroptics.ru ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20