Skip to main content
Log in

Atmospheric correction of remote sensing imagery based on the surface spectrum’s vector space

  • Research Paper
  • Published:
Science China Earth Sciences Aims and scope Submit manuscript

Abstract

Due to the atmosphere effect, the qualities of images decrease conspicuously, practically in the visible bands, in the processing of earth observation by the satellite-borne sensors. Thus, removing the atmosphere effects has become a key step to improve the qualities of images and to retrieve the actual reflectivity of surface features. An atmospheric correction approach, called ACVSS (Atmospheric Correction based Vector Space of Spectrum), is proposed here based on the vector space of the features’ spectrum. The reflectance image of each band is retrieved first according to the radiative transfer equation, then the spectrum’s vector space is constructed using the infrared bands, and finally the residual errors of the reflectance images in the visible bands are corrected based on the pixel position in the spectrum’s vector space. The proposed methodology is verified through atmospheric correction on Landsat-7 ETM+ imagery. The experimental results show that our method is more accurate and the corrected image is more distinct, compared with those offered by current popular atmospheric correction software.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Xu X R. Physical Theory of Remote Sensing (in Chinese). Beijing: Peking University Press, 2006. 292–383

    Google Scholar 

  2. Liang S L, Fang H L, Chen M Z. Atmospheric correction of Landsat ETM+ land surface imagery-Part I: methods. IEEE Trans Geosci Remote Sens, 2001, 39: 2490–2498

    Article  Google Scholar 

  3. Li D R, Wang M, Hu F. Utilizing Chinese high-resolution satellite images for inspection of unauthorized constructions in Beijing. Chin Sci Bull, 2009, 54: 2524–2534

    Article  Google Scholar 

  4. Vermote E, Tanré D, Deuzé J L, et al. Second Simulation of the Satellite Signal in the Solar Spectrum (6S) User’s Guide. UST de Lille, 59655 Villeneuve d’aseq. Laboratoire d’ Optique Atmospherique, 1997

  5. Koutsias N, Mallinis G, Karteris M. A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces. ISPRS J Photogr Remote Sens, 2009, 64: 37–46

    Article  Google Scholar 

  6. Wu J, Wang D, Bauer M E. Image-based atmospheric correction of QuickBird imagery of Minnesota cropland. Remote Sens Environ, 2005, 99: 315–325

    Article  Google Scholar 

  7. Niang A, Badran F, Moulin C, et al. Retrieval of aerosol type and optical thickness over the Mediterranean from SeaWiFS images using an automatic neural classification method. Remote Sens Environ, 2006, 100: 82–94

    Article  Google Scholar 

  8. Tanre D, Herman M, Deschamps P Y. Influence of the background contribution upon space measurements of ground reflectance. Appl Opt, 1981, 20: 3676–3684

    Article  Google Scholar 

  9. Keller J, Bojinski S, Prevot A S H. Simultaneous retrieval of aerosol and surface optical properties using data of the Multi-angle Imaging SpectroRadiometer (MISR). Remote Sens Environ, 2007, 107: 120–137

    Article  Google Scholar 

  10. Kawata Y, Fukui H, Takemata K, et al. Surface reflectance ratios between visible and infrared bands of satellite images over land areas in Japan for retrieval of aerosol optical thickness. Adv Space Res, 2005, 36: 773–777

    Article  Google Scholar 

  11. Chen C, Wu Y H, Liu Z M, et al. The ground reflectance spectrum retrieval from ETM images (in Chinese). Spectr Spec Analysis, 2007, 27: 739–743

    Google Scholar 

  12. He L M, Li X W, Yan G J, et al. Atmospheric correction for AMTIS based on BRDF loop and MODTRAN4.1 (in Chinese). J Remote Sens, 2004, 8: 389–396

    Google Scholar 

  13. Wang X Q, Yang S Z, Zhu Y H, et al. Aerosol optical thickness retrieval over land from MODIS data based on the inversion of the 6S model (in Chinese). Chin J Quantum Electronics, 2003, 20: 529–634

    Google Scholar 

  14. Ghulam A, Qin Q M, Zhu L J. 6S model based atmospheric correction of visible and near-infrared data and sensitivity analysis (in Chinese). Acta Sci Natural Univ Pekinesis, 2004, 40: 611–618

    Google Scholar 

  15. Richter R. A fast atmospheric correction algorithm applied to landsat TM images. Int J Remote Sensing, 1990, 11: 159–166

    Article  Google Scholar 

  16. Tong Q X. Spectrum of Chinese Typical Surface Features and Its Characters Analysis (in Chinese). Beijing: Science Press, 1990. 30–150

    Google Scholar 

  17. Zhao X, Liang S H, Liu S H, et al. Improvement of dark object method in atmospheric correction of hyperspectral remotely sensed data. Sci China Ser D-Earth Sci, 2008, 51: 349–356

    Article  Google Scholar 

  18. Wen J G, Liu Q H, Xiao Q, et al. Modeling the land surface reflectance for optical remote sensing data in rugged terrain. Sci China Ser D-Earth Sci, 2008, 51: 1169–1178

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chun Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, C., Liu, C. & Zhang, S. Atmospheric correction of remote sensing imagery based on the surface spectrum’s vector space. Sci. China Earth Sci. 55, 1289–1296 (2012). https://doi.org/10.1007/s11430-012-4413-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11430-012-4413-4

Keywords

Navigation