Polarimetric SAR interferometry has been successful and attractive for forest parameters (tree height and canopy extinction) estimation. In this paper, we propose to use the ESPRIT algorithm to extract the interferometric phase of local scatterers with polarimetric and interferometric SAR data. Two or three local scattering waves can be extracted at each image patch when a fully polarimetric data set (HH, HV, VV) is available. Furthermore, the ESPRIT can estimate two dominant local scattering centers when only a dual polarimetric data set (e.g., VV and VH) is provided. In order to demonstrate effectiveness the proposed technqiue, we examined the relation between local scattering centers extracted by this method and complex coherence of the coherent scattering model for vegetation cover. The results show that the three-wave estimation can be more accurate than the two-wave case. The extracted interferometric phases with full and dual polarization data sets correspond to effective ground and canopy scattering centers. In this investigation, SIR-C/X-SAR data of the Tien Shan flight-pass are used.

In this paper, we examine the polarimetric characteristics and the potential of the coherent decomposition in polarimetric synthetic aperture radar (SAR) interferometry. Coherent scattering decomposition based on the coherence optimization can separate effective phase center of different scattering mechanisms and can be used to generate canopy digital elevation model (DEM). This decomposition is applied to a simplified stochastic scattering model such as forest canopy. However, since the polarimetric characteristics are not well understood when the decomposition is carried out, we investigate its characteristics and potential using polarimetric entropy-alpha and three-component scattering matrix decomposition. The results show that the first and third components correspond to the lower and upper layer, respectively, in ideal case. In this investigation, SIR-C/X-SAR data of the Tien Shan flight-pass are used.

In Synthetic Aperture Radar Interferometry (InSAR), phase unwrapping holds the key to accurate inversion of digital elevation data. Two new techniques are introduced in this paper that can perform automatic phase unwrapping. The first one is an "optimal" branch-cut algorithm and the second one a hybrid branch-cut/least-square technique, in which pole locations form the weighting basis for the weighted least-square approach. Application of both techniques to ERS-1 data indicates that the height inversion errors are comparable and offer over fifty percent reduction in root mean square (rms) height error compared to the straight least squares method and over thirty-five percent reduction in rms height error compared to the weighted least squares method based on coherence data weighting schemes. The hybrid technique is especially appealing due to its computational efficiency and robustness when compared to traditional branch-cut algorithms.