Extracting Compact Information from Image Benchmarking Tools: The SAR Despeckling Case

  • Gerardo Di Martino
  • Giovanni Pecoraro
  • Giovanni Poggi
  • Daniele Riccio
  • Luisa Verdoliva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


Image databases and benchmarks are precious tools to assess the quality of competing algorithms and to fine tune their parameters. In some cases, however, quality cannot be captured by a single measure, and several of them, providing typically contrasting indications, must be computed and analyzed. This is certainly the case for the SAR despeckling field, also because of the lack of clean reference images, which forces one to compute the measures of interest on simple canonical scenes. We present here the first results of an ongoing work aimed at selecting a suitable combination of benchmark measures to assess competing SAR despeckling techniques and rank them. The full validation of the proposed methodology will require the involvement of a reasonable number of expert photo-interpreters for a large-scale experimental campaign. Here, we present only a sample experiment to provide some insight about the approach.


Reproducible research image benchmarking SAR despeckling 


  1. 1.
    Vandewalle, P., Kovacevic, J., Vetterli, M.: Reproducible research in signal processing. IEEE Signal Processing Magazine 26, 37–47 (2009)CrossRefGoogle Scholar
  2. 2.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Process. 13, 600–612 (2004)CrossRefGoogle Scholar
  3. 3.
    Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: 8th IEEE International Conference on Computer Vision, ICCV (2001)Google Scholar
  4. 4.
    Scarpa, G., Haindl, M.: Unsupervised texture segmentation by spectral-spatial-independent clustering. In: 18th International Conference on Pattern Recognition, vol. 2, pp. 151–154 (2006)Google Scholar
  5. 5.
    Mikes, S., Haindl, M., Scarpa, G.: Remote sensing segmentation benchmark. In: 7th IAPR International Workshop on Pattern Recognition in Remote Sensing (PRRS 2012), Tsukuba Science City, Japan (November 2012)Google Scholar
  6. 6.
    Handbook on constructing composite indicators. Methodology and user guide, OECD/EC JRC (2008)Google Scholar
  7. 7.
    Van Leeuwen, T.N., Visser, M.S., Moed, H.F., Nederhof, T.J., Van Raan, A.F.J.: The Holy Grail of science policy: exploring and combining bibliometric tools in search of scientific excellence. Scientometrics, 257–280 (2003)Google Scholar
  8. 8.
    Cagnazzo, M., Parrilli, S., Poggi, G., Verdoliva, L.: Cost and advantages of shape adaptive wavelet transform in object-based image coding. EURASIP Journal of Image and Video Processing, 1–13 (2007)Google Scholar
  9. 9.
    Di Martino, G., Poderico, M., Poggi, G., Riccio, D., Verdoliva, L.: Benchmarking framework for SAR despeckling. IEEE Trans. Geosci. Remote Sens. (in Press, 2013)Google Scholar
  10. 10.
    Franceschetti, G., Migliaccio, M., Riccio, D., Schirinzi, G.: SARAS: a SAR raw signal simulator. IEEE Trans. Geosci. Remote Sens. 30(1), 110–123 (1992)CrossRefGoogle Scholar
  11. 11.
    Lopes, A., Touzi, R., Nezry, E.: Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosci. Remote Sens., 992–1000 (1990)Google Scholar
  12. 12.
    Deledalle, C.A., Denis, L., Tupin, F.: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Process. 18, 2661–2672 (2009)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Parrilli, S., Poderico, M., Angelino, C.V., Scarpa, G., Verdoliva, L.: A nonlocal approach for SAR image denoising. In: Proc. IGARSS, pp. 726–729 (July 2010)Google Scholar
  14. 14.
    Parrilli, S., Poderico, M., Angelino, C.V., Verdoliva, L.: A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 50, 606–616 (2012)CrossRefGoogle Scholar
  15. 15.
    Cozzolino, D., Parrilli, S., Scarpa, G., Poggi, G., Verdoliva, L.: Fast adaptive nonlocal SAR despeckling. IEEE Geosci. Remote Sens. Lett. (in Press, 2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gerardo Di Martino
    • 1
  • Giovanni Pecoraro
    • 1
  • Giovanni Poggi
    • 2
  • Daniele Riccio
    • 1
  • Luisa Verdoliva
    • 1
  1. 1.DIETIUniversity Federico II of NaplesNaplesItaly
  2. 2.Accademia AeronauticaPozzuoliItaly

Personalised recommendations