Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2014, Vol. 11, No. 4, pp. 360-368

The effectiveness of atmospheric correction for Hyperion hyperspectral images in regions with developed vegetation cover

A.A. Derkacheva  , O.V. Tutubalina 
M.V. Lomonosov Moscow State University, Moscow, Russia
We review the most significant sources of atmospheric influence in remotely sensed imagery and various algorithms for image atmospheric correction, including IARD, QUAC, FLAASH, and EL. These algorithms are applied to an EO-1 Hyperion image of 27 July 2013 of a forested territory in central Kola Peninsula, north-west Russia. We conclude that the empirical line (EL) regression algorithm, which uses field spectroradiometric data for two signatures, yielded the best results, on the basis of the assessment with other field data. We provide recommendations for choosing ground signature areas: they should be sufficient in area, homogeneous, have sufficient range of spectral radiance values (as a set of signatures in total), be stable over time.
Keywords: hyperspectral images, atmospheric correction algorithms, ground spectroradiometry data
Full text

References:

  1. Zabelin S.A., Tulegulov A.D., Metodika atmosfernoi korrektsii snimkov Landsat (Technique of atmospheric correction for Landsat imagery), Vestnik ENU im. L.N. Gumileva, 2011 (6), pp.147-154.
  2. Schowengerdt R.A., Distantsionnoe zondirovanie. Modeli i metody obrabotki izobrazheniy (Remote sensing: Models and methods for image processing), Moscow: Tekhnosfera, 2010, 560 p.
  3. Adler-Golden S., Bernstein L., Matthew M., Atmospheric compensation of extreme off-nadir hyperspectral imagery, SPIE, Proceedings, Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery XIII, 2007.
  4. Beck R. EO-1 User Guide, v. 2.3, Satellite Systems Branch USGS EDS, 2003.
  5. Exelis Visual Information Solutions, Atmospheric Correction, Exelis VIS, 2014. http://www.exelisvis.com/.
  6. Kawishwar P. Atmospheric Correction Models for Retrievals of Calibrated Spectral Profiles from Hyperion, India: Dehra Dun, Indian Institute of remote sensing, National Remote Sensing Agency, 2007.
  7. San B.T., Suzen M.L., Evaluation of different atmospheric correction algorithms for EO-1 Hyperion imagery, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Part 8, Kyoto, 2010, pp. 392-397.
  8. Tuominen J., T. Lipping T., Atmospheric correction of hyperspectral data using combined empirical and model based method, Tampere University of Technology, 2004.
  9. Xu Y., Wang R., Liu Sh., Yang S., Yan B., Atmospheric correction of hyperspectral data using MODTRAN model, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 2008.