Hierarchical Segmentation of Polarimetric SAR Images Using Heterogeneous Clutter Models

International audienceIn this paper, heterogeneous clutter models are used to describe polarimetric synthetic aperture radar (PolSAR) data. The KummerU distribution is introduced to model the PolSAR clutter. Then, a detailed analysis is carried out to evaluate the potential of this new multivariate distribution. It is implemented in a hierarchical maximum likelihood segmentation algorithm. The segmentation results are shown on both synthetic and high-resolution PolSAR data at the X- and L-bands. Finally, some methods are examined to determine automatically the "optimal" number of segments in the final partition

Heterogeneous Clutter Models for Change Detection in PolSAR Imagery

International audienceThe new generation of Synthetic Aperture Radar (RADARSAT-2, TerraSAR-X, ALOS, . . . ) allows us to capture Earth surface images with very high resolution. Therefore the possibility to characterize objects has become more and more attainable. Moreover, the short revisit time propertie of these satellites enables the development of techniques of change detection and their applications. Spherically Invariant Random Vector (SIRV) model was designed specifically for the analysis of heterogeneous clutters in high resolution radar images. In this paper, we propose four algorithms of change detection based on different criteria including: Gaussian (sample covariance matrix estimator), Gaussian (fixed point estimator), Fisher texture-based and KummerU-based (Fisher distributed texture)

Performance of the maximum likelihood estimators for the parameters of multivariate generalized Gaussian distributions

International audienceThis paper studies the performance of the maximum likelihood estimators (MLE) for the parameters of multivariate generalized Gaussian distributions. When the shape parameter belongs to ]0,1[, we have proved that the scatter matrix MLE exists and is unique up to a scalar factor. After providing some elements about this proof, an estimation algorithm based on a Newton-Raphson recursion is investigated. Some experiments illustrate the convergence speed of this algorithm. The bias and consistency of the scatter matrix estimator are then studied for different values of the shape parameter. The performance of the shape parameter estimator is finally addressed by comparing its variance to the Cramér-Rao bound

DEM error retrieval by analyzing time series of differential interferograms

International audience2-pass Differential Synthetic Aperture Radar Interferometry (D-InSAR) processing have been successfully used by the scientific community to derive velocity fields. Nevertheless, a precise Digital Elevation Model (DEM) is necessary to remove the topographic component from the interferograms. This letter presents a novel method to detect and retrieve DEM errors by analyzing time series of differential interferograms. The principle of the method is based on the comparison of fringe patterns with the perpendicular baseline. First, a mathematical description of the algorithm is exposed. Then, the algorithm is applied on a series of four one-day ERS-1/2 interferograms

Displacement Estimation by Maximum Likelihood Texture Tracking

International audienceThis paper presents a novel method to estimate displacement by maximum-likelihood (ML) texture tracking. The observed polarimetric synthetic aperture radar (PolSAR) data-set is composed by two terms: the scalar texture parameter and the speckle component. Based on the Spherically Invariant Random Vectors (SIRV) theory, the ML estimator of the texture is computed. A generalization of the ML texture tracking based on the Fisher probability density function (pdf) modeling is introduced. For random variables with Fisher distributions, the ratio distribution is established. The proposed method is tested with both simulated PolSAR data and spaceborne PolSAR images provided by the TerraSAR-X (TSX) and the RADARSAT-2 (RS-2) sensors

KummerU clutter model for PolSAR data: Application to segmentation and classification

International audienceIn this paper, Spherically Invariant Random Vectors (SIRV) are introduced to describe the heterogeneity of the Polarimetric Synthetic Aperture Radar (PolSAR) clutter. In this context, the scalar texture parameter and the normalized covariance matrix are extracted from the PolSAR images. If the texture parameter is modeled by a Fisher Probability Density Function (PDF), the observed target scattering vector follows a KummerU PDF. This PDF is then implemented in a hierarchical segmentation algorithm. Finally, segmentation results are shown on both synthetic and real images

Segmentation and Classification of Polarimetric SAR Data based on the KummerU Distribution

International audienceThinner spatial features can be observed from the high resolution of newly available spaceborne and airborne SAR images. Heterogeneous clutter models should be used to model the covariance matrix because each resolution cell contains only a small number of scatterers. In this paper, we focus on the use of a Fisher probability density function (pdf) to model the SAR clutter. First, the benefit of using such a pdf is exposed. Covariance matrix statistics are then analyzed in details. For a Fisher distributed texture, the covariance matrix follows a KummerU pdf. Asymptotic cases of this pdf are presented. Finally, the KummerU pdf is implemented in both hierarchical segmentation and classification algorithms. Segmentation and classification results are shown on both synthetic and real data

Segmentation and Classification of Polarimetric SAR Data based on the KummerU Distribution

International audienceThinner spatial features can be observed from the high resolution of newly available spaceborne and airborne SAR images. Heterogeneous clutter models should be used to model the covariance matrix because each resolution cell contains only a small number of scatterers. In this paper, we focus on the use of a Fisher probability density function (pdf) to model the SAR clutter. First, the benefit of using such a pdf is exposed. Covariance matrix statistics are then analyzed in details. For a Fisher distributed texture, the covariance matrix follows a KummerU pdf. Asymptotic cases of this pdf are presented. Finally, the KummerU pdf is implemented in both hierarchical segmentation and classification algorithms. Segmentation and classification results are shown on both synthetic and real data