Skip to main content
SpringerLink
Log in

Linear Spectral Mixture Analysis of Hyperspectral Images with Atmospheric Distortions

  • Conference paper
  • First Online:
Computer Vision and Graphics (ICCVG 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9972))

Included in the following conference series:

  • 1403 Accesses

Abstract

In this paper a novel method of linear spectral mixture parameters estimation for hyperspectral images is proposed. This method allows to omit preliminary atmospheric correction of input image. To provide a solution to mixture problem different models of radiation transmission in atmosphere are considered. An evaluation of effect of noise, number of input pixels and number of signatories on accuracy of restored linear mixture coefficients and input pixel representation error is provided.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Matthew, M.V., Adler-Golden, S.M., Berk, A., Felde, G., Anderson, G.P., Gorodetzky, D., Paswaters, S., Shippert, M.: Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data. In: Applied Imagery Pattern Recognition Workshop, 2002, AIPR, 31 February 2002, pp. 157–163. IEEE (2002)

    Google Scholar 

  2. Kruse, F.A.: Comparison of ATREM, ACORN, and FLAASH atmospheric corrections using low-altitude AVIRIS data of Boulder, CO. In: Summaries of 13th JPL Airborne Geoscience Workshop, Jet Propulsion Lab, Pasadena, CA (2004)

    Google Scholar 

  3. Belov, A.M., Myasnikov, V.V.: Atmospheric correction of hyperspectral images based on approximate solution of transmittance equation. In: 7th International Conference on Machine Vision (ICMV 2014), pp. 94450S–94450S5. International Society for Optics and Photonics (2015)

    Google Scholar 

  4. Chang, C.I.: Hyperspectral Data Processing: Algorithm Design and Analysis. Wiley, Hoboken (2013)

    Book  MATH  Google Scholar 

  5. Keshara, N.A.: Survey of spectral unmixing algorithms. Lincoln Lab. J. 14(1), 55–78 (2003)

    Google Scholar 

  6. Denisova, A., Myasnikov, V.: Fully constrained linear spectral unmixing algorithm for hyperspectral image analysis. Comput. Opt. 38(4), 782–789 (2014)

    Article  Google Scholar 

  7. Yuanliu, X., Runsheng, W., Shengwei, L., Suming, Y., Bokun, Y.: Atmospheric correction of hyperspectral data using MODTRAN model. Proc. SPIE Int. Soc. Opt. Eng. 7123, 7123061–7123067 (2003)

    Google Scholar 

  8. Kruse, F.A., Boardman, J.W., Huntington, J.F.: Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Trans. Geosci. Remote Sens. 41(6), 1388–1400 (2003)

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by the Russian Science Foundation (RSF), grant no. 14-31-00014 Establishment of a Laboratory of Advanced Technology for Earth Remote Sensing.

Author information

Authors and Affiliations

  1. Samara National Research University, 34 Moskovskoe Shosse, Samara, 443086, Russian Federation

    Anna Denisova, Yuliya Juravel & Vladislav Myasnikov

Authors
  1. Anna Denisova
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuliya Juravel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vladislav Myasnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Denisova .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences , Warsaw, Poland

    Leszek J. Chmielewski

  2. Computer Sci and Software Engineering, University of Western Australia , Perth, Australia

    Amitava Datta

  3. Faculty of Applied Informatics and Math, Warsaw University of Life Sciences SGGW , Warsaw, Poland

    Ryszard Kozera

  4. Institute of Computer Science, Silesian University of Technology , Gliwice, Poland

    Konrad Wojciechowski

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Denisova, A., Juravel, Y., Myasnikov, V. (2016). Linear Spectral Mixture Analysis of Hyperspectral Images with Atmospheric Distortions. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-46418-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46417-6

  • Online ISBN: 978-3-319-46418-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation