Decomposition of Polarimetric SAR Images Based on Second- and Third-order Statics Analysis

There are many papers concerning the research of the decomposition of polerimetric SAR imagery. Most of them are based on second-order statics analysis that Freeman and Durden [1] suggested for the reflection symmetry condition that implies that the co-polarization and cross-polarization correlations are close to zero. Since then a number of improvements and enhancements have been proposed to better understand the underlying backscattering mechanisms present in polarimetric SAR images. For example, Yamaguchi et al. [2] added the helix component into Freeman's model and developed a 4 component scattering model for the non-reflection symmetry condition. In addition, Arii et al. [3] developed an adaptive model-based decomposition method that could estimate both the mean orientation angle and a degree of randomness for the canopy scattering for each pixel in a SAR image without the reflection symmetry condition. This purpose of this research is to develop a new decomposition method based on second- and third-order statics analysis to estimate the surface, dihedral, volume and helix scattering components from polarimetric SAR images without the specific assumptions concerning the model for the volume scattering. In addition, we evaluate this method by using both simulation and real UAVSAR data and compare this method with other methods. We express the volume scattering component using the wire formula and formulate the relationship equation between backscattering echo and each component such as the surface, dihedral, volume and helix via linearization based on second- and third-order statics. In third-order statics, we calculate the correlation of the correlation coefficients for each polerimetric data and get one new relationship equation to estimate each polarization component such as HH, VV and VH for the volume. As a result, the equation for the helix component in this method is the same formula as one in Yamaguchi's method. However, the equation for the volume component is different from other methods. This method can estimate each polarization component for the volume without specific a priori volume scattering assumptions. References [1] A. Freeman and S. L. Durden, "A three-component scattering model for polarimetric SAR data," IEEE Trans. Geosci. Remote Sens., vol.36, no.3, pp. 964-973, May 1998. [2] Y. Yamaguchi, T. Moriyama, M. Ishido and H. Yamada, "Four-component scattering model for polarimetric SAR image decomposition," IEEE Trans. Geosci. Remote Sens., vol.43, no.8, pp. 1699-1706, Aug. 2005. [3] Motofumi Arii, Jakob J. van Zyl and H. Yamada, "Adaptive model-based decomposition of polarimetric SAR covariance matrices," IEEE Trans. Geosci. Remote Sens., vol.49, no.3, pp. 1104-1113, Mar. 2011. Acknowledgment This research was conducted at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.