Spectral Overlap Correction Using Weighted Deconvolution for Improved Dark Object Technique for Correction of EO1-Hyperion Data

  • Shailesh S. Deshpande
  • Arun B. Inamdar
  • Krishna Mohan Buddhiraju
Research Article


We present a critical modification to improved dark object technique for correcting hyperspectral data (EO1-Hyperion). The modification is required in improved dark object technique as the original method does not take into account overlap of spectral response functions of two adjacent bands of hyperspectral sensor. We used weighted deconvolution for correcting the original overlap affected path radiance correction propagation factors. Further, we compared the reduction in correction factors—in different conditions—because of the overlap. We calculated the path radiance for April 22 Hyperion image and compared it with other methods such as 6SV. We found noticeable difference in corrected and uncorrected path radiance propagation factors with “clear” to “very clear” atmospheric models. For the other models (“moderate”, “hazy”, “very hazy”), the difference is negligible and can be ignored and improved dark object technique can be applied without any overlap correction.


EO1-Hyperion Improved dark object technique Overlapping bands Spectral deconvolution 



Shailesh Deshpande would like to thank Dr. Daniel Schläpfer for valuable discussion on spectral deconvolution, and Dr. David Jupp for providing useful links for Hyperion spectral response functions. He would also like to thank Prof. Harrick Vin for encouragement and support throughout the work. We thank principal investigators for their effort in establishing and maintaining AERONET Pune site.


  1. AERONET. (2015). AERONET—Aerosol robotic network. Accessed December 16, 2015.
  2. Beck, R. A. (2003). EO-1 user guide v. 2.3. Accessed December 16, 2015.
  3. Chavez, P. S., Jr. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3), 459–479.CrossRefGoogle Scholar
  4. Chavez, P. S., Jr. (1996). Image-based atmospheric corrections—Revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1036.Google Scholar
  5. Gao, B.-C., Montes, M. J., Davis, O. C., & Goetz, A. F. (2009). Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sensing of Environment, 113, s17–s24.CrossRefGoogle Scholar
  6. Goetz, A. F., Vane, G., Solomon, J. E., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147–1153.CrossRefGoogle Scholar
  7. Kruse, F. A. (2004). Comparison of ATREM, ACORN, AND FLAASH atmospheric corrections using low-altitude data of Boulder CO. 13th JPL airborne geoscience workshop, JPL Publication 05-3. Pasadena, CA: Jet Propulsion Laboratory.Google Scholar
  8. Lee, C. S., Yeom, J. M., Lee, H. L., Kim, J.-J., & Han, K.-S. (2015). Sensitivity analysis of 6S-based look-up table for surface reflectance retrieval. Asia-Pacific Journal of Atmospheric Sciences, 51(1), 91–101.CrossRefGoogle Scholar
  9. Moran, M., Jackson, R. D., Slater, P. N., & TeiUet, P. M. (1992). Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of Environment, 41, 169–184.CrossRefGoogle Scholar
  10. Penn, B. S. (2002). Applying band ratios to hyperspectral data. 2002 Denver Annual Meeting, Denver. Accessed January 2016.
  11. Richards, J. A., & Jia, X. (2006). Chapter 2. Correction of radiometric distortion. In Berlin et al (Ed.), Remote sensing digital image analysis: an introduction (4th ed., p. 34). Berlin: Springer. Google Scholar
  12. Schläpfer, D., Boerner, A., & Schaepman, M. (1999). The potential of spectral resampling techniques for the simulation of APEX imagery based on AVIRIS data. In Summaries of the eighth JPL airborne earth science workshop. JPL Publication 99-17.Google Scholar
  13. Senthil Kumar, A., Radhika, T., Keerthi, V., Jain, D. S., Dadhwal, V. K., & Kiran Kumar, A. S. (2013). Spectral deconvolution and non-overlap bands sampling for IMS-1 hyperspectral imager. Journal of Indian Society of Remote Sensing. doi: 10.1007/s12524-013-0345-5.Google Scholar
  14. USGS, U.S. Geological Survey. (2013, April 22). EO1 Hyperion scene: EO1H1470472013112110KZ_PF1_01, for target path 147, and target row 47. Sioux Falls, SD: U. S. Geological Survey.Google Scholar
  15. USGS. (2011). Earth Observing 1 (EO-1)-FAQs. Accessed December 1, 2015.
  16. USGS EO1. Accessed April 30, 2014.
  17. Vane, G., & Goetz, A. F. (1988). Terrestrial imaging spectroscopy. Remote Sensing of Environment, 24(1), 1–29.CrossRefGoogle Scholar
  18. Vermote, E., Tanré, D., Deuzé, J. L., Herman, M., Morcrette, J. J., & Kotchenova, S. Y. (2006). Second simulation of a satellite signal in the solar spectrum-vector (6SV), 6S User Guide, Version 3. Accessed December 1, 2015.
  19. Vincent, R. K. (1972). An ERTS multispectral scanner experiment for mapping iron compounds. In Eighth international symposium on remote sensing of environment (pp. 1239–1247), Ann Arbor, MI.Google Scholar
  20. Vincent, R. K., Quin, X., McKay, R. L., Miner, J., Czajkowski, K., Savino, J., et al. (2004). Phycocyanin detection from LANDSAT TM data for mapping cyanobacterial blooms in Lake Erie. Remote Sensing of Environment, 89, 381–392.CrossRefGoogle Scholar
  21. Weisstein, E. W. (2017). Gaussian function. From MathWorld—A Wolfram Web Resource. Accessed July 23, 2017.
  22. Wilson, R. T. (2012). Py6S: A Python interface to the 6S radiative transfer model. Computers & Geosciences, 51, 166–171.CrossRefGoogle Scholar
  23. Zhao, W., Tamura, M., & Takahashi, H. (2000). Atmospheric and spectral corrections for estimating surface albedo from satellite data using 6S code. Remote Sensing of Environment, 76, 202–212.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2017

Authors and Affiliations

  • Shailesh S. Deshpande
    • 1
    • 2
  • Arun B. Inamdar
    • 2
  • Krishna Mohan Buddhiraju
    • 2
  1. 1.Tata Research Development and Design Centre (A Division of Tata Consultancy Services)PuneIndia
  2. 2.Centre of Studies in Resources Engineering, Indian Institute of Technology (IIT), BombayPowai, MumbaiIndia

Personalised recommendations