ADVANCED CONCEPTS IN POLARIMETRY – PART 1 (Polarimetric Target Description, Speckle filtering and Decomposition Theorems)

There is currently a great deal of interest in the use of polarimetry for radar remote sensing. In this context, different and important objectives are to classify Earth terrain components within a fully polarimetric SAR image and then extract physical information from the observed scattering of microwaves by surface and volume structures. The most important observable measured by such radar systems is the 3x3-coherency matrix [T]. This matrix accounts for local variations in the scattering matrix and is the lowest order operator suitable to extract polarimetric parameters for distributed scatterers in the presence of additive (system) and/or multiplicative (speckle) noise. In the first part of this paper, the most important Target Polarimetry descriptors: Sinclair Matrix, target vectors, coherency matrix and the covariance matrix as well are presented, their interconnections and equivalences will be shown together with the respective transformations. Speckle appearing in synthetic aperture radar (SAR) images is due to the coherent interference of waves reflected from many elementary scatterers and causes degradation and makes automatic image segmentation and scene description difficult. The speckle reduction problem is more complicated for polarimetric SAR than a single polarization SAR, because of the difficulties of preserving polarimetric properties and of dealing with the cross-product terms. The first part of this paper is ended by a presentation and a description of polarimetric speckle filters preserving polarimetric properties and statistical correlation between channels, not introducing crosstalk, and not degrading the image quality. The impact of using this polarimetric speckle filtering on terrain classification is quite dramatic in boosting classification performance. Many targets of interest in radar remote sensing require a multivariate statistical description due to the combination of coherent speckle noise and random vector scattering effects from surface and volume. For such targets, it is of interest to generate the concept of an average or dominant scattering mechanism for the purposes of classification or inversion of scattering data. Target Decomposition theorems are aimed at providing such an interpretation based on sensible physical constraints such as the average target being invariant to changes in wave polarization basis. Among the existing Polarimetric Target Decomposition theorems coherent (Krogager, Cameron ...), non-coherent (Huynen, Barnes ...) model-based decomposition (Freeman) or eigenvector-based decomposition (Cloude, Van Zyl) the H/A/α Decomposition Theorem, proposed by S.R. Cloude and E. Pottier in 1997 for extracting average parameters from experimental data is presented and discussed. Based on an eigenvalues analysis of the coherency matrix, this decomposition theorem employs a 3-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters.