DUAL FREQUENCY POLARIMETRIC SAR DATA CLASSIFICATION AND ANALYSIS

In this paper, we introduce a new classification scheme for dual frequency polarimetric SAR data sets. A (6×6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6 by 6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class characteristics. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the crosscorrelation matrix. The analysis of the resulting clusters is realized by determining the rigorous change in polarimetric properties from one image to the other. The polarimetric variations are parameterized by 8 real coefficients derived from the decomposition of a special unitary operator on the Gell-Mann basis. These classification and analysis schemes are applied to full polarimetric P, L, and C bands SAR images of the Nezer forest acquired by NASA/JPL AIRSAR sensor (1989). 248 Ferro-Famil and Pottier

[1]  Konstantinos P. Papathanassiou,et al.  Polarimetric SAR interferometry , 1998, IEEE Trans. Geosci. Remote. Sens..

[2]  Simon Yueh,et al.  Application of neural networks to radar image classification , 1994, IEEE Trans. Geosci. Remote. Sens..

[3]  Eric Pottier,et al.  A review of target decomposition theorems in radar polarimetry , 1996, IEEE Trans. Geosci. Remote. Sens..

[4]  Yunhan Dong,et al.  A new decomposition of radar polarization signatures , 1998, IEEE Trans. Geosci. Remote. Sens..

[5]  S. Cloude.  Group theory and polarisation algebra , 1986 .

[6]  J. Zyl,et al.  Unsupervised classification of scattering behavior using radar polarimetry data , 1989 .

[7]  Kun-Shan Chen,et al.  Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network , 1996, IEEE Trans. Geosci. Remote. Sens..

[8]  Jin Au Kong,et al.  Classification of Earth Terrain Using Polarimetric Synthetic Aperture Radar Images , 1989, Progress In Electromagnetics Research.

[9]  Stephen L. Durden,et al.  Mapping Sub-Tropical Vegetation Using Multi-Frequency, Multi-Polarization Sar Data , 1992, [Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium.

[10]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[11]  Rama Chellappa,et al.  Unsupervised segmentation of polarimetric SAR data using the covariance matrix , 1992, IEEE Trans. Geosci. Remote. Sens..

[12]  N. R. Goodman.  Statistical analysis based on a certain multivariate complex Gaussian distribution , 1963 .

[13]  Jiancheng Shi,et al.  Estimation of snow water equivalence using SIR-C/X-SAR , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[14]  Stephen L. Durden,et al.  Three-component scattering model to describe polarimetric SAR data , 1993, Optics & Photonics.

[15]  J. Zyl,et al.  Bayesian classification of polarimetric SAR images using adaptive a priori probabilities , 1992 .

[16]  A. W. Joshi,et al.  Elements of group theory for physicists , 1974 .

[17]  J. Huynen.  Phenomenological theory of radar targets , 1970 .

[18]  R. Muirhead.  Aspects of Multivariate Statistical Theory , 1982, Wiley Series in Probability and Statistics.

[19]  Jakob J. van Zyl,et al.  Calibrated imaging radar polarimetry: technique, examples, and applications , 1991, IEEE Trans. Geosci. Remote. Sens..

[20]  Laurent Ferro-Famil,et al.  Application of Polarimetric SAR Data Processing to Snow Cover Remote Sensing. Validation Using Optical Images and Ground Data , 1999 .

[21]  Eric Pottier,et al.  An entropy based classification scheme for land applications of polarimetric SAR , 1997, IEEE Trans. Geosci. Remote. Sens..