Sophie Allain

Overview of the PolSARpro V4.0 software. the open source toolbox for polarimetric and interferometric polarimetric SAR data processing

The objective of this paper is to make a review of the current status of the PolSARpro v4.0 Software (Polarimetric SAR Data Processing and Educational Toolbox), developed under contract to ESA by a consortium comprising I.E.T.R at the University of Rennes 1, AELc, DLR-HR and Dr mark Williams from Adelaide. The objective of this current project is to provide Educational Software that offers a tool for self-education in the field of Polarimetric SAR data analysis at University level and a comprehensive suite of functions for the scientific exploitation of fully and partially polarimetric multi-data sets and the development of applications for such data. The PolSARpro v4.0 Software establishes a foundation for the exploitation of Polarimetric techniques for scientific developments and stimulates research and applications developments using PolSAR and PolInSAR data.

Snowpack Characterization in Mountainous Regions Using C-Band SAR Data and a Meteorological Model

This paper presents a method to characterize snow cover in mountainous regions using dual-polarization C-band synthetic aperture radar (SAR) data. It is demonstrated that an accurate modeling of the liquid water distribution inside the snowpack, using a multilayer meteorological snow model, is required to characterize snow with precision. A multilayer-snow electromagnetic (EM) backscattering model is developed based on the vector radiative transfer, the strong fluctuation theory, and physical parameters supplied by the meteorological model. However, the limited resolution of the meteorological snow model is insufficient for predicting a refined EM backscattering at a massif scale. An adequate spatial reorganization of these snow profiles, based on a comparison between simulated and measured dual-polarization SAR data, leads to a better estimation of some snowpack parameters. In particular, the monitoring of snow liquid water content is presented improving the capacity of wet snow mapping as compared to a classical SAR-based method. This methodology shows good capacities both for qualitative and quantitative snow assessments, opening the way for a new operational method.

Classification of river ice using polarimetric SAR data

Ice jams are a major cause of river flooding in Canada. These events can be devastating for the environment, human infrastructure, and population. Although methodologies have been developed to discriminate ice types using single-polarization synthetic aperture radar (SAR) data, SAR polarimetry has not yet been used. In this paper a polarimetric SAR airborne image of the Saint-François River, Quebec, has been analyzed. Complementary data about the characteristics of the ice cover were obtained simultaneously with the image acquisition. The usefulness of each polarimetric parameter is explored to obtain realistic ice type classifications. We propose to compute a rule-based hierarchical classification and compare it with a Wishart classification. A single-polarization-based classification is also used to show the limits of this approach in discriminating water from ice. The hierarchical classification more accurately separates areas of ice from areas of open water (81% producer’s accuracy). Both classifications show good results, with few ambiguities in detection of the consolidated ice class. Detection of the thermal ice class is not highly accurate. Thermal and frazil ice classification is performed better when hierarchical classification than when Wishart classification is used. Lastly, the hierarchical classification is better adapted to river ice than Wishart classification, and fully polarimetric data are significantly better than single-polarization data for discriminating water from ice.

Surface parameter retrieval from polarimetric and multi-frequency SAR data

The aim of this paper is to present a surface model inversion using the integral equation formulation of backscattering coefficients. A quantification of the influence of surface parameters such as roughness and soil moisture on polarimetric indicators for various frequency bands is led. Finally, a technique is introduced to retrieve a surface RMS height and dielectric constant from multi-frequency data. The inversion technique is applied to polarimetric and multi-frequency measurements acquired at EMSL, JRC laboratory.

Extraction of surface parameters from multi-frequency and polarimetric SAR data

The aim of this paper is to analyse the information contained in multi-frequency and polarimetric SAR data for an accurate retrieval of surface geophysical parameters. A two-scale surface scattering model is presented with the aim to develop an inversion algorithm. This model is represented by projection into a three-dimensional space defined by the H-A-/spl alpha/_parameters, which permits, for each observation frequency value, to obtain distinct curves with respect to the soil moisture content and large-scale roughness. Then, a comparison between multi-frequency and polarimetric data from the JRC laboratory and the two-scale surface polarimetric response is carried out. At last, two different multi-frequency parameter inversion methods based on artificial neural networks schemes are proposed.

New eigenvalue-based parameters for natural media characterization

The aim of this paper is to present two novel polarimetric parameters, the eigenvalue relative difference (ERD) and the single bounce eigenvalue relative difference (SERD), to characterize natural media. These parameters are derived from the eigen-decomposition of the coherency matrix considering the reflection symmetry hypothesis. An analysis of these parameters is performed on multi-frequency polarimetric SAR data acquired on bare soils and forested areas.

Capabilities of Full-Polarimetric PALSAR/ALOS for Snow Extent Mapping

Snow classification using full-polarimetric PALSAR data is investigated in this paper. It is first demonstrated that dry snowpack over frozen ground slightly affects polarimetric signature at L-band. Given the fact that PALSAR data do not permit the use of a simplistic threshold-based method, a refined method for Snow Covered Area mapping is outlined. A supervised Support Vector Machine approach is used showing fairly good results within the framework of a three-classes classification (dry snow over frozen ground, wet snow and no snow).

Influence of resolution cell size for surface parameters retrieval from polarimetric SAR data

This paper introduces a study of the influence of the size of SAR resolution cell on polarimetric scattering characteristics over rough surfaces. Surface scattering is shown to be dependent on the cell size to correlation length ratio. SAR resolution is taken into account by dividing a surface spectrum in two parts: a low-frequency spectrum corresponding to local slopes and a high-frequency component, defining the roughness inside a resolution cell. Backscattering coefficients are calculated for each resolution cell with the IEM model using local incidence angles. Surface scattering is characterized with three polarimetric indicators H/A//spl alpha//spl I.bar/, highly related to the soil characteristics. These models are validated on indoor polarimetric SAR measurements acquired at the JRC laboratory.

Surface Roughness and Microwave Surface Scattering of High-Resolution Imaging Radar

This study aims to understand the effects of spatial resolution on the surface backscattering characteristics of polarimetric radar. Surface scattering models based on approximate methods are formulated by the roughness second-order statistics to obtain a closed-form expression for the radar scattering response. Most studies have been carried out based on the roughness parameters of the infinite surface. In this letter, we propose the roughness autocorrelation function of truncated surfaces for a more realistic description of the roughness parameters of high-resolution radar. The use of roughness parameters for a truncated surface in the scattering model is pertinent to explain the dependence of the backscattering coefficient on the spatial resolution. Simulation results indicate that the traditional computation of the surface backscattering based on the autocovariance function of an infinite surface leads to an underestimation of the backscattering signature of the high-resolution radar.

Retrieval of river ice thickness from C-band PolSAR data

Until now, existing models for retrieving river ice thickness are mostly based on environmental data. They require many inputs and indicate a global value of ice thickness for a large heterogeneous area. Studies have been performed intending to retrieve river ice thickness throughout remote sensing using monopolarized C-band radar data. But no reliable ice maps of ice thickness have been produced. In this paper, the information gain from polarimetric SAR data is demonstrated and a river ice thickness model is proposed. This model is applied and validated on Radarsat-2 images acquired at C-band in winter 2009 over the Saint-François River (Southern Quebec), the Kosoak River (Northern Quebec) and the Mackenzie River (Northwest Territories), in Canada. Field campaigns were carried out to obtain more than 70 samples of various river ice thickness. The optimal polarimetric parameter is chosen to retrieve both easily and rapidly river ice thickness. This approach offers reliable spatially distributed ice maps.