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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, 2012, Vol. 9, No. 2, pp. 298-303

Improvement of AMSR-E SST by deconvolution

A.I. Alexanin , V. Kim 
Institute for Automation and Control Processes FEB RAS, 690041 Vladivostok, 5 Radio str
The substantial overlapping of adjacent AMSR-E footprints and low resolution of AMSR-E sensors make it possible to improve AMSR-E SST accuracy by deconvolution. The Wiener filter is suggested to compensate the influence of AMSR-E point spread function. Kashevarov's bank has been used as a test region. The noise and blurring have been considered as main sources of errors. As a standard we use SST based on IR MODIS data. It was shown that near Kashevarov's bank AMSR-E SST is higher than MODIS SST up to 3°К. Deconvolution of AMSR-E data can significantly reduce this kind of errors.
Keywords: sea surface temperature, AMSR-E sensor, improvement of SST accuracy, restoration problem, Wiener filter
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References:

  1. Mitnik M.L., Mitnik L.M., Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2005, Vol. 1, pp. 244-249.
  2. Bensebaa K., Banon G., Fonseca L., On-orbit spatial resolution estimation of CBERS-1 CCD camera, Proc. Third International Conference on Image and Graphics, Hong Kong, 2004, pp. 576-579.
  3. Canny J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, Vol. 8, No. 6, pp. 679–698.
  4. Fish D.A., Brinicombe A.M., Pike E.R., Walker J.G., Blind deconvolution by means of the Richardson–Lucy algorithm, Journal of the Optical Society of America, 1995, Vol. 12, No. 1, pp. 58-65.
  5. Gentemann C. L., Meissner T., Wentz F. J., Accuracy of Satellite Sea Surface Temperatures at 7 and 11 GHz, IEEE Transactions on Geoscience and Remote Sensing, 2010, Vol. 48, No. 3, pp. 1009-1018.
  6. Gunn, B.A., Long D.G., Spatial Resolution Enhancement of AMSR Tb Images based on Measurement Local Time of Day, Proceedings of IGARSS, Boston, 2008, Vol. 5, pp. 33-36.
  7. Kundur D., Hatzinakos D., Blind image deconvolution, IEEE Signal Processing Magazine, 1996, Vol. 13, No. 3, pp. 43–64.
  8. Limaye A.S., Crosson W.L., Lyamon C.A., Estimating accuracy in optimal deconvolution of synthetic AMSR-E observation, Remote Sensing of Environment, 2006, Vol. 100, pp. 133-142.
  9. Long D. G., Daum D. L., Spatial Resolution Enhancement of SSM/I Data, IEEE Transactions on Geoscience and Remote Sensing, 1998, Vol. 35, No. 2, pp. 407-417.
  10. Santi E., Brogioni M., Macelloni G., Paloscia S., Pampaloni P., Pettinato S., A simple technique to improve the AMSR-E spatial resolution at C-band, Microwave Radiometry and Remote Sensing of the Environment, 2008, pp. 1-4.
  11. Shibata A.A., AMSR/AMSR-E SST algorithm developments: removal of ocean wind effect, Italian J. Rem. Sens., 2004, Vol. 30/31, pp. 131–142.
  12. Wentz F.J., Meissner T., Algorithm theoretical basis document for AMSR-E ocean algorithms, RSS Tech. Rep., May 17, 2007, pp. 1–6.
  13. Williams B.A., Long D.G., Reconstruction From Aperture-Filtered Samples With Application to Scatterometer Image Reconstruction, IEEE Transactions on Geoscience and Remote Sensing, 2011, Vol. 49, No. 5, pp. 1663-1676.