Adaptive Algorithm-Based Fused Bayesian Maximum Entropy-Variational Analysis Methods for Enhanced Radar Imaging

  • R. F. Vázquez-Bautista
  • L. J. Morales-Mendoza
  • R. Ortega-Almanza
  • A. Blanco-Ortega
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)


In this paper we address an adaptive computational algorithm to improve the Bayesian maximum entropy–variational analysis (BMEVA) performance for high resolution radar imaging and denoising. Furthermore, the variational analysis (VA) approach is aggregated by imposing the metrics structures in the corresponding signal spaces. Then, the formalism for combining the Bayesian maximum entropy strategy with the VA paradigm is presented. Finally, the image enhancement and denoising benefits produced by the proposed Adaptive Bayesian maximum entropy–variational analysis (ABMEVA) method are showed via simulations with real-world radar scene


Bayesian Maximum Entropy data fusion adaptive algorithm variational analysis 


  1. 1.
    Skolnic, M.I. (ed.): Radar Handbook, 2nd edn. McGraw-Hill, Boston (1990)Google Scholar
  2. 2.
    Shkvarko, Y.V.: Estimation of Wavefield Power Distribution in the Remotely Sensed Environment: Bayesian Maximum Entropy Approach. IEEE Trans. on Signal Processing 50(9), 2333–2346 (2002)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ben Hamza, A., Krim, H., Unal, G.B.: Unifying Probabilistic and Variational Estimation. IEEE Signal Processing Magazine 19, 37–47 (2002)CrossRefGoogle Scholar
  4. 4.
    Black, M., Sapiro, G., Marimont, D.H., Hegger, D.: Robust Anisotropic Diffusion. IEEE Trans. Image Processing 7(3), 421–432 (1998)CrossRefGoogle Scholar
  5. 5.
    Khuong Nguyen, M., Mohammad-Djafari, A.: Bayesian Approach with the Maximum Entropy Principle in Image Reconstruction from Microwave Scattered Field Data. IEEE Trans. Medical Imaging 13, 2 (1994)CrossRefGoogle Scholar
  6. 6.
    Shkvarko, Y.V.: Estimation of Wavefield Power Distribution in the Remotely Sensed Environment: Bayesian Maximum Entropy Approach. IEEE Transactions on Signal Processing 50, 2333–2346 (2002)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Shkvarko, Y.V.: Unifying Regularization and Bayesian Estimation Methods for Enhanced Imaging with Remotely Sensed Data. Part I – Theory. IEEE Transactions on Geoscience and Remote Sensing 42, 923–931 (2004)CrossRefGoogle Scholar
  8. 8.
    Vazquez-Bautista, R.F., Morales-Mendoza, L.J., Shkvarko, Y.V.: Aggregating the Statistical Estimation and Variational Analysis Methods in Radar Imagery. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS, Toulouse, France, vol. 3, pp. 2008–2010 (2003)Google Scholar
  9. 9.
    Morales-Mendoza, L.J., Vazquez-Bautista, R.F., Shkvarko, Y.V.: Unifying the Maximum Entropy and Variational Analysis Regularization Methods for Reconstruction of the Remote Sensing Imagery. IEEE Latin America Transactions 3, 60–73 (2005)CrossRefGoogle Scholar
  10. 10.
    Shkvarko, Y., Vazquez-Bautista, R., Villalon-Turrubiates, I.E.: Fusion of Bayesian Maximum Entropy Spectral Estimation and Variational Analysis Methods for Enhanced Radar Imaging. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2007. LNCS, vol. 4678, pp. 109–120. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Cichocki, A., Amari, S.-i.: Adaptive Blind Signal and Image Processing. John Wiley & Sons, England (2002)CrossRefGoogle Scholar
  12. 12.
    Solberg, A.H.S.: Data Fusion for Remote-Sensing Applications. In: Chen, C.H. (ed.) Signal and Image Processing for Remote Sensing, pp. 515–537. CRC Press, Boca Raton (2007)Google Scholar
  13. 13.
    Nadine, M.: Minimum Variance. In: Castanié, F. (ed.) Spectral Analysis: Parametric and Non-Parametric Digital Methods, ISTE USA, 1st edn., pp. 175–211 (2006)Google Scholar
  14. 14.
    Likas, A., Galatsanos, N.: Bayesian Methods based on Variational approximation for Blind Image Deconvolution. In: Campisi, P., Egiazarian, K. (eds.) Blind Image Deconvolution:Theory and Applications, pp. 141–168. CRC Press, Boca Raton (2007)CrossRefGoogle Scholar
  15. 15.
    Joshi, M., Jalobeanu, A.: MAP Estimation for Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior Model. IEEE Transactions on Geoscience and Remote Sensing 48(3), 1245–1255 (2010)CrossRefGoogle Scholar
  16. 16.
    Plaza, A., Chang, C.I.: High-Performance Computer Architectures for Remote Sensing Data Analysis: Overview and Case Study. In: Plaza, A., Chang, C.I. (eds.) High Performance Computing in Remote Sensing, USA, pp. 9–41. Chapman & Hall/CRC (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • R. F. Vázquez-Bautista
    • 1
  • L. J. Morales-Mendoza
    • 1
  • R. Ortega-Almanza
    • 1
  • A. Blanco-Ortega
    • 2
  1. 1.FIECUniversidad VeracruzanaVer
  2. 2.CENIDET-Ingeniería MecatrónicaCuernavaca

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