Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

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Synthetic aperture radar interferometry

The authors examine the role of polarimetry in synthetic aperture radar (SAR) interferometry. They first propose a general formulation for vector wave interferometry that includes conventional scalar interferometry as a special case. Then, they show how polarimetric basis transformations can be introduced into SAR interferometry and applied to form interferograms between all possible linear combinations of polarization states. This allows them to reveal the strong polarization dependency of the interferometric coherence. They then solve the coherence optimization problem involving maximization of interferometric coherence and formulate a new coherent decomposition for polarimetric SAR interferometry that allows the separation of the effective phase centers of different scattering mechanisms. A simplified stochastic scattering model for an elevated forest canopy is introduced to demonstrate the effectiveness of the proposed algorithms. In this way, they demonstrate the importance of wave polarization for the physical interpretation of SAR interferograms. They investigate the potential of polarimetric SAR interferometry using results from the evaluation of fully polarimetric interferometric shuttle imaging radar (SIR)-C/X-SAR data collected during October 8-9, 1994, over the SE Baikal Lake Selenga delta region of Buriatia, Southeast Siberia, Russia.

Polarimetric SAR interferometry

A four-component scattering model is proposed to decompose polarimetric synthetic aperture radar (SAR) images. The covariance matrix approach is used to deal with the nonreflection symmetric scattering case. This scheme includes and extends the three-component decomposition method introduced by Freeman and Durden dealing with the reflection symmetry condition that the co-pol and the cross-pol correlations are close to zero. Helix scattering power is added as the fourth component to the three-component scattering model which describes surface, double bounce, and volume scattering. This helix scattering term is added to take account of the co-pol and the cross-pol correlations which generally appear in complex urban area scattering and disappear for a natural distributed scatterer. This term is relevant for describing man-made targets in urban area scattering. In addition, asymmetric volume scattering covariance matrices are introduced in dependence of the relative backscattering magnitude between HH and VV. A modification of probability density function for a cloud of dipole scatterers yields asymmetric covariance matrices. An appropriate choice among the symmetric or asymmetric volume scattering covariance matrices allows us to make a best fit to the measured data. A four-component decomposition algorithm is developed to deal with a general scattering case. The result of this decomposition is demonstrated with L-band Pi-SAR images taken over the city of Niigata, Japan.

Four-component scattering model for polarimetric SAR image decomposition

Four-Component Scattering Model for Polarimetric SAR Image Decomposition based on Asymmetric Covariance Matrix(Remote sensing/Radar (2), Workshop for Space, Aeronautical and Navigational Electronics (WSANE 2005))

Examines 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.

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Single-baseline polarimetric SAR interferometry

The authors propose a new method for unsupervised classification of terrain types and man-made objects using polarimetric synthetic aperture radar (SAR) data. This technique is a combination of the unsupervised classification based on polarimetric target decomposition, S.R. Cloude et al. (1997), and the maximum likelihood classifier based on the complex Wishart distribution for the polarimetric covariance matrix, J.S. Lee et al. (1994). The authors use Cloude and Pottier's method to initially classify the polarimetric SAR image. The initial classification map defines training sets for classification based on the Wishart distribution. The classified results are then used to define training sets for the next iteration. Significant improvement has been observed in iteration. The iteration ends when the number of pixels switching classes becomes smaller than a predetermined number or when other criteria are met. The authors observed that the class centers in the entropy-alpha plane are shifted by each iteration. The final class centers in the entropy-alpha plane are useful for class identification by the scattering mechanism associated with each zone. The advantages of this method are the automated classification, and the interpretation of each class based on scattering mechanism. The effectiveness of this algorithm is demonstrated using a JPL/AIRSAR polarimetric SAR image.

Unsupervised classification using polarimetric decomposition and the complex Wishart classifier

This paper addresses the problem of polarimetric SAR (POLSAR) data correction for changes in radar cross sections, which are caused by azimuth slopes. Most radiometric slope corrections remove slope effects to account for the effective scattering pixel area. However, few studies address the slope effect on the radar cross section as a function of polarization states. The authors propose two approaches to compensate for this azimuth slope effect for POLSAR data. In the first approach, the digital elevation model (DEM), obtained from interferometric SAR or from other means, is used to estimate orientation angles, and in the second approach, orientation angles are derived directly from the POLSAR data. They have developed three new methods to implement this second approach. One is based on the right-right and left-left circular polarizations, and the other two are based on Cloude's and Huynelt's decompositions. The results are compared with the original polarization signature method using the DEM-generated orientation angles as the reference. POLSAR data is then compensated using the derived orientation angles. Significant changes were observed in many elements of the coherency matrix. The compensated POLSAR data should improve the accuracy of geophysical parameter estimation techniques. NASA/JPL AIRSAR data for an area in central California is used for illustration.

Polarimetric SAR data compensation for terrain azimuth slope variation

This paper addresses the problem of polarimetric SAR (POLSAR) data correction for changes in radar cross sections, which are caused by azimuth slopes. Most radiometric slope corrections remove slope effects to account for the effective scattering pixel area. However, few studies address the slope effect on the radar cross section as a function of polarization states. We propose two approaches to compensate for this azimuth slope effect for POLSAR data. In the first approach, the digital elevation model (DEM), obtained from interferometric SAR or from other means, is used to estimate orientation angles, and in the second approach, orientation angles are derived directly from the POLSAR data. We have developed three new methods to implement this second approach. One is based on the right-right and left-left circular polarizations, and the other two are based on Cloude's and Huynen's decompositions. The results are compared with the original polarization signature method using the DEM-generated orientation angles as the reference. POLSAR data is then compensated using the derived orientation angles. Significant changes were observed in many elements of the coherency matrix. The compensated POLSAR data should improve the accuracy of geophysical parameter estimation techniques. NASA/JPL AIRSAR data for an area in central California is used for illustration.

In recent studies, D. L. Schuler et al. (2000) applied polarimetric imaging radar-derived orientation angles to measure topography, and J. S. Lee et al. (2000) used orientation angles for polarimetric SAR data compensation, to ensure accurate estimation of geophysical parameters in rugged terrain areas. To support these applications, it is important to accurately estimate shifts in orientation angles induced by the azimuth slope variations. However, in many cases, inconsistency in the estimation of orientation angle shifts was encountered in several areas, introducing noisy and erroneous results. The present authors develop a unified analysis of estimation algorithms based on the circular polarization covariance matrix. The concept of reflection symmetry is used to explain the soundness of the circular polarization method and to show problems associated with other algorithms. L-band polarimetric synthetic aperture radar (SAR) images of Camp Roberts, CA, are used to substantiate this theory.

On the estimation of radar polarization orientation shifts induced by terrain slopes

A new concept in polarimetric synthetic aperture radar (POLSAR) speckle filtering that preserves the dominant scattering mechanism of each pixel is proposed in this paper. The basic principle is to select pixels of the same scattering characteristics to be included in the filtering process. To achieve this, the algorithm first applies the Freeman and Durden decomposition to separate pixels into three dominant scattering categories: surface, double bounce, and volume, and then unsupervised classification is applied. Speckle filtering is performed using the classification map as a mask. A single-look or multilook pixel centered in a 9 /spl times/ 9 window is filtered by including only pixels in the same and two neighboring classes from the same scattering category. This filter is effective in speckle reduction, while perfectly preserving strong point target signatures, and retains edges, linear, and curved features in the POLSAR data. The effect of speckle filtering on scattering characteristics, such as entropy, anisotropy, and alpha angle, will be discussed.

Dale L. Schuler

Papers

Unsupervised classification using polarimetric decomposition and the complex Wishart classifier

Polarimetric SAR data compensation for terrain azimuth slope variation

Polarimetric SAR data compensation for terrain azimuth slope variation

On the estimation of radar polarization orientation shifts induced by terrain slopes

Scattering-model-based speckle filtering of polarimetric SAR data