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The objective of this paper is to examine the application of single-baseline polarimetric SAR interferometry to the remote sensing and measurement of structure over forested terrain. For this, a polarimetric coherent scattering model for vegetation cover suitable for the estimation of forest parameters from interferometric observables is introduced, discussed and validated. Based on this model, an inversion algorithm which allows the estimation of forest parameters such as tree height, average extinction, and underlying topography from single-baseline fully polarimetric interferometric data is addressed. The performance of the inversion algorithm is demonstrated using fully polarimetric single baseline experimental data acquired by DLR's E-SAR system at L-band.

The authors provide a new geometrical approach for the inversion of a two-layer coherent scattering model, widely used for the interpretation of polarimetric interferometric S A R data. It has been shown in several recent publications that, by using interferograms in multiple polarisation channels, estimation of vegetation height, underlying ground topography and mean extinction is possible. Furthermore, this can be achieved with a single frequency sensor without the need for a separate reference DEM, other a priori information or the use of data-specific regression formulas. The authors first review the details of this approach and then develop a three-stage inversion procedure to illustrate the steps involved in parameter estimation. They then consider several possible sources of error in the inversion. In particular, they concentrate on the effects of vertical tree structure and on the effects of temporal decorrelation on inversion accuracy. It is shown that the former leads to errors, mainly in the extinction estimation, while the latter does not change the model structure but reduces the available parameter set and increases the variance of the parameter estimates. Finally, the new algorithm is applied to simulated vector coherent SAR data for a random canopy.

This paper proposes a new model for the inversion of surface roughness and soil moisture from polarimetric synthetic aperture radar (SAR) data, based on the eigenvalues and eigenvectors of the polarimetric coherency matrix. It demonstrates how three polarimetric parameters, namely the scattering entropy ( ), the scattering anisotropy ( ), and the alpha angle ( ) may be used in order to decouple surface roughness from moisture content estimation offering the possibility of a straightforward inversion of these two surface parameters. The potential of the proposed inversion algorithm is investigated using fully polarimetric laboratory measurements as well as airborne L-band SAR data and ground measurements from two different test sites in Germany, the Elbe-Auen site and the Weiherbach site.Index Terms-Inversion, soil moisture, surface parameters, surface roughness, synthetic aperture radar (SAR) polarimetry.

In this paper, we propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric S A R data. This technique is a combination of the unsupervised classification based on the polarimetric target decomposition (Cloude and Pottier, 1997) and the maximum likelihood classifier based on the complex Wishart distribution (Lee et al., 1994). The advantage of this approach is that clusters may be identified by the scattering mechanisms from the target decomposition. The effectiveness of this algorithm is demonstrated using JPUAIRSAR and SIR-C polarimetric SAR images. MTRODUCTION Terrain classification is an important polarimetric SAR (POLSAR) application.Many algorithms have been developed for supervised and unsupervised terrain classification. In principle, training sets for each class are selected manually in the supervised classification. This manual technique could be difficult to implement due to the high dimensionality of POLSAR imagery. Unsupervised classification automatically finds clusters based on a certain criterion. In most algorithms, clusters have to be interpreted manually for terrain types to be useful.Cloude and Pottier [l] proposed an unsupervised classification algorithm based on their target decomposition theory. The medium's scattering mechanisms, characterized by entropy (H) and a angle are used for classification. The H and a plane is divided into 8 zones. The physical scattering characteristic associated with each zone provides information for terrain type assignment. This distinctive advantage, unfortunately, is offset by preset zone boundaries in H and a plane. Clusters may fall on the boundary or more than one class may be enclosed in one zone. Furthermore, magnitudes are not used in the classification.Lee et a1 [2] developed a supervised algorithm based on the complex Wishart distribution for the polarimetric covariance matrix. Training sets have to be selected in advance.We propose to use the Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map serves as training sets for classification based on the Wishart distribution. The classified results are then used as training sets for the next iteration. Significant improvement in each iteration has been observed. The iteration ends, when the number of pixels switching classes becomes smaller than a predetermined number. We have observed that the class centers in the H -E plane are shifted after the first and subsequent iterations. The class boundaries in the H -a plane become indistinct with considerable overlap. However, the final class centers in H -E plane are useful for interpretation of terrain types. The advantage of this combined method is in its effectiveness in automated classification, and in providing interpretation based on scattering mechanism for each class. Classification Based on Target DecompositionCloude and Pottier's decomposition is based on the eigenvalue analysis of the coherency matrix, T, which is formed by the Pauli base...