Validation with Airborne Data

  • Armando Marino
Part of the Springer Theses book series (Springer Theses)


In the previous section, the statistics of the detector were derived to establish its theoretical performance. Although the assessment returned promising results, the validation on real data is an unavoidable step, since in real scenarios the performance of an algorithm can be dramatically degraded by factors which cannot be easily taken into account in a theoretical model.


Bare Ground Multiple Reflection Real Target Standard Target Vertical Dipole 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Attema EPW, Ulaby FT (1978) Vegetation modeled as water cloud. Radio Sci 13:357–364CrossRefGoogle Scholar
  2. Cameron WL (1996) Simulated polarimetric signatures of primitive geometrical shapes. IEEE Trans Geosci Remote Sensing 34:793–803CrossRefGoogle Scholar
  3. Campbel JB (2007) Introduction to remote sensing. The Guilford Press, New YorkGoogle Scholar
  4. Chaney RD, Bud MC, Novak LM (1990) On the performance of polarimetric target detection algorithms. IEEE Aerospace Electron Syst Mag 5:10–15CrossRefGoogle Scholar
  5. Cloude SR (1987) Polarimetry: the characterisation of polarisation effects in EM scattering. Electronics Engineering Department, University of York, YorkGoogle Scholar
  6. Cloude RS (1995a) An introduction to wave propagation & antennas. UCL Press, LondonGoogle Scholar
  7. Cloude SR (1995b) Lie groups in EM wave propagation and scattering. In: Baum C, Kritikos HN (eds) Electromagnetic symmetry. Taylor and Francis, Washington, pp 91–142. ISBN 1-56032-321-3 (chapter 2)Google Scholar
  8. Cloude SR (2009) Polarisation: applications in remote sensing. Oxford University Press, Oxford. ISBN 978-0-19-956973-1Google Scholar
  9. Cloude SR, Pottier E (1997) An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans Geosci Remote Sensing 35:68–78CrossRefGoogle Scholar
  10. Cloude SR, Corr DG, Williams ML (2004) Target detection beneath foliage using polarimetric synthetic aperture radar interferometry. Waves Random Complex Media 14:393–414Google Scholar
  11. Curlander JC, Mc donough RN (1991) Synthetic aperture radar: systems and signal processing. Wiley, New YorkGoogle Scholar
  12. Dong Y, Forster B (1996) Understanding of partial polarization in polarimetric SAR data. Int J Remote Sens 17:2467–2475CrossRefGoogle Scholar
  13. Durden SL, Van Zyl JJ, Zebker HA (1999) Modeling and observations of the radar polarization signatures of forested areas. IEEE Trans Geosci Remote Sensing 27:2363–2373Google Scholar
  14. Fleischman JG, Ayasli S, Adams EM (1996) Foliage attenuation and backscatter analysis of SAR imagery. IEEE Trans Aerospace Electron Syst 32:135–144CrossRefGoogle Scholar
  15. Fung AK, Ulaby FT (1978) A scatter model for leafy vegetation. IEEE Trans Geosci Electron 16:281–286 CrossRefGoogle Scholar
  16. Hajnsek I, Schön H, Jagdhuber T, Papathanassiou K (2007) Potentials and limitations of estimating soil moisture under vegetation cover by means of PolSAR. In: Fifth international symposium on retrieval of bio and geophysical parameters from SAR data for land applications, Bari, September 2007Google Scholar
  17. Horn R, Nannini M, Keller M (2006) SARTOM Airborne campaign 2006: data acquisition report. DLR-HR-SARTOM-TR-001Google Scholar
  18. Huynen JR (1970) Phenomenological theory of radar targets. Delft Technical University, DelftGoogle Scholar
  19. Kay SM (1998) Fundamentals of statistical signal processing. Volume 2: detection theory. Prentice Hall, Englewood CliffsGoogle Scholar
  20. Lang RH (1981) Electromagnetic scattering from a sparse distribution of lossy dielectric scatterers. Radio Sci 16:15–30CrossRefGoogle Scholar
  21. Lee JS, Pottier E (2009) Polarimetric radar imaging: from basics to applications. CRC Press, Boca RatonGoogle Scholar
  22. Li J, Zelnio EG (1996) Target detection with synthetic aperture radar. IEEE Trans Aerospace Electron Syst 32:613–627CrossRefGoogle Scholar
  23. Mott H (2007) Remote sensing with polarimetric radar. Wiley, HobokenGoogle Scholar
  24. Novak LM, Hesse SR (1993) Optimal polarizations for radar detection and recognition of targets in clutter. In: Proceedings of IEEE national radar conference, Lynnfield, MA, pp 79–83Google Scholar
  25. Novak LM, Burl MC, Irving MW (1993) Optimal polarimetric processing for enhanced target detection. IEEE Trans Aerospace Electron Syst 20:234–244CrossRefGoogle Scholar
  26. Novak LM, Owirka GJ, Weaver AL (1999) Automatic target recognition using enhanced resolution SAR data. IEEE Trans Aerospace Electron Syst 35:157–175CrossRefGoogle Scholar
  27. Papathanassiou KP, Cloude SR (2001) Single-baseline polarimetric SAR interferometry. IEEE Trans Geosci Remote Sensing 39:2352–2363CrossRefGoogle Scholar
  28. Rose HE (2002) Linear algebra: a pure mathematical approach. Birkhauser, BerlinGoogle Scholar
  29. Rothwell EJ, Cloud MJ (2001) Electromagnetics. CRC Press, Boca RatonGoogle Scholar
  30. Strang G (1988) Linear algebra and its applications, 3rd edn. Thomson Learning, USAGoogle Scholar
  31. Stratton JA (1941) Electromagnetic theory. McGraw-Hill, New YorkGoogle Scholar
  32. Treuhaft RN, Cloude RS (1999) The structure of oriented vegetation from polarimetric interferometry. IEEE Trans Geosci Remote Sensing 37:2620–2624CrossRefGoogle Scholar
  33. Treuhaft RN, Siqueria P (2000) Vertical structure of vegetated land surfaces from interferometric and polarimetric radar. Radio Sci 35:141–177CrossRefGoogle Scholar
  34. Tsang L, Kong JA, Shin RT (1985) Theory of microwave remote sensing. Wiley, New YorkGoogle Scholar
  35. Ulaby FT, Elachi C (1990) Radar polarimetry for geo-science applications. Artech House, NorwoodGoogle Scholar
  36. Woodhouse IH (2006) Introduction to microwave remote sensing. CRC Press, Taylor and Francis Group, Boca RatonGoogle Scholar
  37. Zebker HA, Van Zyl JJ (1991) Imaging radar polarimetry: a review. In: Proceedings of the IEEE, p 79Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg  2012

Authors and Affiliations

  • Armando Marino
    • 1
  1. 1.ETH ZurichInstitute of Environmental EngineeringZurichSwitzerland

Personalised recommendations