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Volume 22 Issue 3
May  2000
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Liu Guoqing, Xiong Hong, Huang Shunji. AUTOMATIC MULTIRESOLUTION CLASSIFICATION OF POLARIMETRIC SAR IMAGE WITH WAVELET TRANSFORM AND MARKOV RANDOM FIELD[J]. Journal of Electronics & Information Technology, 2000, 22(3): 359-365.
Citation: Liu Guoqing, Xiong Hong, Huang Shunji. AUTOMATIC MULTIRESOLUTION CLASSIFICATION OF POLARIMETRIC SAR IMAGE WITH WAVELET TRANSFORM AND MARKOV RANDOM FIELD[J]. Journal of Electronics & Information Technology, 2000, 22(3): 359-365.

AUTOMATIC MULTIRESOLUTION CLASSIFICATION OF POLARIMETRIC SAR IMAGE WITH WAVELET TRANSFORM AND MARKOV RANDOM FIELD

  • Received Date: 1998-10-16
  • Rev Recd Date: 1999-05-22
  • Publish Date: 2000-05-19
  • In this paper an automatic multiresolution classification method is proposed to classify polarimetric synthetic aperture radar (SAR) image.At first a textured SAR image characterizing the terrain reflection is obtained by using a multi-look polarimetric whitening filter (MPWF) to reduce the speckle in the polarimetric SAR image. Then the wavelet transform (WT) is utilized to extract texture information in different resolutions, and in the lowest resolution level the Akaik information criterion (AIC) is used for estimating the optimal number of texture classes in the image. Next the Markov random field (MRF) model is employed to characterize the spatial constraints between pixels in each resolution level, and a maximum like- lihood (ML) approach and an iterated conditional mode (ICM) approach are used for the model parameters estimation and maximum a posteriori (MAP) classification, respectively. Finally the paper presents the experimental results with the NASA/JPL L-band airborne polarimetric SAR data and verifies the effectiveness and advantage of the classification method proposed.
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  • Novak L M, Burl M C. Optimal speckle reduction in polarimetric SAR imagery[J].IEEE Trans. on Aerospace Electrical Systems.1990, 26(2):293-305[2]Guoqing Liu, Shunji Huang, Torre A, Rubertone F. The multi-look polarimetric whitening filter for speckle reduction in polarimetric SAR image .IEEE Trans. on Geoscience and Remote Sensing, 1998,363, 36(3) :1016-1020.[3]Mallat S G. A theory for multiresolution signal decomposition: the wavelet representation .IEEE Trans. on Pattern Anal. and Machine Intell, 1989,117, 11(7) :674-693.[4]Daubechies I. The wavelet transform, time-frequency location and signal analysis .IEEE Trans. on Information Theory, 1990,365, 36(5) :961-1005.[5]Besag J. Spatial interaction and the statistical analysis of lattice system .J. Roy. Statist. Soc. B, 1974,362, 36(2) :192-236.[6]Besag J. On the statistical analysis of dirty data .J. Roy. Statist. Soc. B, 1986,483, 48(3) :259-302.[7]Bouman C, Liu B. Multiple resolution segmentation of texture images .IEEE Trans. on Pattern Anal. and Machine Intell, 1991,132, 13(2) :99-113.[8]Geman S, Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images .IEEE Trans. on Pattern Anal. and Machine Intell, 1984,66, 6(6) :721-741.[9]Akaik H. A new look at the statistical model identification.IEEE Trans. ou Automat .Control, 1974,196, 19(6) :716-723.[10]Zhang J, Modestino J W, Langan D A. Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation .IEEE Trans. on Image Processing, 1994,34, 3(4) :404-420.[11]Ulaby F T, Elachi C. Radar Polarimetry for Geoscience Applications, Norwood, MA: Artech House Inc, 1990, Chapter 2, 24-25.[12]Srivastava S. On the complex Wishart distribution .Annals of Mathematical Statistics, 1965,362, 36(2) :313-315.[13]Guoqing Liu.[J].Shunji Huang, Torre A, Rubertone F. DuberOptimal multi-look polarimetric speckle reduction and its effect on terrain classification, in Proc. of IGARSS96, Lincoln, NE, USA: July.1515,1996:-
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