Eric Pottier

A review of target decomposition theorems in radar polarimetry

In this paper, we provide a review of the different approaches used for target decomposition theory in radar polarimetry. We classify three main types of theorem; those based on the Mueller matrix and Stokes vector, those using an eigenvector analysis of the covariance or coherency matrix, and those employing coherent decomposition of the scattering matrix. We unify the formulation of these different approaches using transformation theory and an eigenvector analysis. We show how special forms of these decompositions apply for the important case of backscatter from terrain with generic symmetries.

An entropy based classification scheme for land applications of polarimetric SAR

The authors outline a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a three-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data. The scattering entropy is a key parameter is determining the randomness in this model and is seen as a fundamental parameter in assessing the importance of polarimetry in remote sensing problems. The authors show application of the method to some important classical random media scattering problems and apply it to POLSAR data from the NASA/JPL AIRSAR data base.

Inversion of surface parameters from polarimetric SAR

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 (H), the scattering anisotropy (A), and the alpha angle (/spl alpha/) 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.

Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H/A/Alpha-Wishart classifier

Introduces a new classification scheme for dual frequency polarimetric SAR data sets. A (6/spl times/6) polarimetric coherency matrix is defined to simultaneously take into account the full polarimetric information from both images. This matrix is composed of the two coherency matrices and their cross-correlation. A decomposition theorem is applied to both images to obtain 64 initial clusters based on their scattering characteristics. The data sets are then classified by an iterative algorithm based on a complex Wishart density function of the 6/spl times/6 matrix. A class number reduction technique is then applied on the 64 resulting clusters to improve the efficiency of the interpretation and representation of each class. An alternative technique is also proposed which introduces the polarimetric cross-correlation information to refine the results of classification to a small number of clusters using the conditional probability of the cross-correlation matrix. These classification schemes are applied to full polarimetric P, L, and C-band SAR images of the Nezer Forest, France, acquired by the NASA/JPL AIRSAR sensor in 1989.

Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR

This paper addresses the land-use classification capabilities of fully polarimetric synthetic aperture radar (SAR) versus dual-polarization and single-polarization SAR for P-, L-, and C-Band frequencies. A variety of polarization combinations will be investigated for application to crop and tree age classification. Based on the complex Wishart distribution for the covariance matrix, maximum likelihood (ML) classifiers for all polarization combinations were used to assess quantitative classification accuracy. Thus, this allows optimally selecting the frequency and the combination of polarizations for various applications.

Scattering-model-based speckle filtering of polarimetric SAR data

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.

Statistical assessment of eigenvector-based target decomposition theorems in radar polarimetry

The performance of quantitative remote sensing based on multidimensional synthetic aperture radars (SARs), and polarimetric SAR systems in particular, depends strongly on a correct statistical characterization of the data, i.e., on a complete knowledge of the effects of the speckle noise. In this framework, the eigendecomposition of the covariance or coherency matrices and the associated H//spl alpha/_/A decomposition have demonstrated the potential for quantitative estimation of physical parameters. In this paper, we present a detailed study of the statistics associated with this decomposition. This analysis requires the introduction of mathematical tools that are not well known in the remote sensing community. For this reason, we include a review section to present them. Using this work, we then present an expression for the probability density function of the sample eigenvalues of the covariance or coherency matrix. The availability of this expression allows a complete study of the separated sample eigenvalues, as well as, the entropy H and the anisotropy A. As demonstrated, all these parameters must be considered as asymptotically nonbiased with respect to the number of looks. In order to reduce the biases for a small number of averaged samples, a novel estimator for the eigenvalues is proposed. The results of this work are analyzed by means of simulated and real airborne SAR data. This analysis permits us to determine in detail the effects of the number of averaged samples in the estimation of physical information in radar polarimetry.

An Unsupervised Segmentation With an Adaptive Number of Clusters Using the

In this paper, an unsupervised segmentation is proposed for fully polarimetric synthetic aperture radar (SAR) data analysis. The backscattering power SPAN combined with H/alpha/A is used to obtain the initial cluster centers. We use the Wishart test statistic to perform an agglomerative hierarchical clustering to obtain the segmentation results with different numbers of clusters. The appropriate number of clusters is automatically estimated using the data log-likelihood (Lm), and the resulting images with the estimated number of clusters are the final segmentation results. The experiments show that the SPAN has additional information that is not contained in H/alpha/A, and this information could be useful for the initialization. The number of clusters seems to be a crucial point for the segmentation, which will affect the segmentation performance. It is also shown that the data log-likelihood has the potential ability to reveal the inner structure of fully polarimetric SAR data.

SPAN/H/\alpha/A

In synthetic aperture radar (SAR) polarimetry, the measured polarimetric signatures are used to analyze physical scattering properties of the imaged media. It is generally assumed that the sensor has a fixed orientation with respect to the objects. However, SAR sensors operating at lower frequencies, like L- and P-band, have a wide azimuth beamwidth, i.e., during the formation of the synthetic aperture, multiple squint angles are integrated to build the full-resolution SAR image. Variations in the polarimetric properties with the azimuthal look angle remain unconsidered. In this paper, a fully polarimetric subaperture analysis method is introduced. Using deconvolution, synthesized SAR images are decomposed into subaperture datasets, which correspond to the scene responses under different azimuthal look angles. A statistical analysis of the polarimetric parameters permits to clearly discriminate media showing a nonstationary behavior during the SAR integration. Finally, a method is proposed, which eliminates the influence of azimuthal backscattering variations in conventional polarimetric SAR data analysis. The effectiveness of the new methods is demonstrated on fully polarimetric SAR data, acquired by the German Aerospace Center (DLR) airborne experimental SAR sensor (E-SAR) at L-band.

Space and the Complex Wishart Clustering for Fully Polarimetric SAR Data Analysis

This letter proposes a building characterization technique for L-band polarimetric interferometric synthetic aperture radar (SAR) data. This characterization consists of building identification and height estimation. Initially, a polarimetric interferometric segmentation is performed to isolate buildings from their surroundings. This classification identifies three basic categories: single bounce, double bounce, and volume diffusion. In order to compensate for the misclassifications among the volume and the double-bounce classes, interferometric phases given by the high-resolution Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) method are analyzed. Once buildings are localized, a phase-to-height procedure is applied to retrieve building height information. The method is validated using E-SAR, German Aerospace Center (DLR) fully polarimetric SAR data, at L-band, repeat-pass mode, over the Oberpfaffenhofen, Germany, test site, with a spatial resolution of 1.5 m in range and azimuth. More than 80% of buildings are retrieved with acceptably accurate height estimates.