The development of Radar Polarimetry and Radar Interferometry is advancing rapidly. Whereas with radar polarimetry, the textural fine-structure, target orientation, symmetries and material constituents can be recovered with considerable improvement above that of standard amplitude-only radar; with radar interferometry the spatial (in depth) structure can be explored. In Polarimetric Interferometric Synthetic Aperture Radar (POL-IN-SAR) Imaging, it is possible to recover such co-registered textural and spatial information from POL-IN-SAR digital image data sets simultaneously, including the extraction of Digital Elevation Maps (DEM) from either Polarimetric (scattering matrix) or Interferometric (single platform: dual antenna) SAR systems. Simultaneous Polarimetric-plus-Interferometric SAR offers the additional benefit of obtaining co-registered textural-plus-spatial three-dimensional POL-IN-DEM information, which when applied to Repeat-Pass Image-Overlay Interferometry provides differential background validation, stress assessment and environmental stress-change information with high accuracy. Then, by either designing Multiple Dual-Polarization Antenna POL-IN-SAR systems or by applying advanced POL-IN-SAR image compression techniques, it will result in POL-arimetric TOMO-graphic (Multi-Inter-ferometric) SAR or POL-TOMO-SAR Imaging. This is of direct relevance to local-to-global environmental background validation, stress assessment and stress-change monitoring of the terrestrial and planetary covers.

Classification of terrain is one of the most important applications of Polarimetric Synthetic Aperture Radar (POLSAR) image analysis. This paper presents a simple method to classify terrain by the use of the correlation coefficients in the circular polarization basis together with the total power of the scattering matrix in the X-band. The reflection symmetry condition that the co-polarized and the cross-polarized correlations are close to zero for natural distributed scatterers is utilized to extract characteristic parameters of small forests or cluster of trees, and oriented urban building blocks with respect to the direction of the radar illumination. Both of these kinds of scatterers are difficult to identify in high resolution POLSAR images of complex urban areas. The indices employed here are the correlation coefficient, a modified coefficient normalized by the reflection symmetric conditional case, and the total power. It is shown that forest areas and oriented building blocks are easily detected and identified. The terrain classification yielded by these combinations is very accurate as confirmed by photographic ground truth images.

The concept of the equi-phase curve is introduced for the cross-polarized channel case. It is proved that the equi-phase curves are a series of half circles on the Poincare sphere, and that all these curves have two common ends. Based on the introduced concept, this letter demonstrates the distribution of the received voltage's phases on the Poincare sphere. In addition, it is shown theoretically that the cross-polarized phase of the off-diagonal elements of a scattering matrix is unstable for most natural targets. Therefore, the cross-polarized phase information cannot be used for extracting target characteristics in polarimetric radar remote sensing.

Synthetic aperture radar interferometry have been established in the past two decades, and used extensively for many applications including topographic mapping of terrain and surface deformation. Vegetation analysis is also a growing area of its application. In this paper, we propose an polarimetric SAR interferometry technique for interferometric phase extraction of each local scatterer. The estimated position of local scattering centers has an important information for effective tree height estimation of forest. The proposed method formulated for local scattering center extraction is based on the ESPRIT algorithm which is known for high-resolution capability of closely located incident waves. The method shows high-resolution performance when local scattered waves are uncorrelated and have different polarization characteristics. Using the method, the number of dominant local scattering centers and interferometric phases in each image pixel can be estimated directly. Validity of the algorithm is demonstrated by using examples derived from SIR-C data.