Search results for: J-P. Ovarlez
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In this paper, we propose a novel methodology for Change Detection between two monovariate complex SAR images. Linear Time-Frequency tools are used in order to recover a spectral and angular diversity of the scatterers present in the scene. This diversity is used in bi-date change detection framework to develop a detector, whose performances are better than the classic detector on monovariate SAR...
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The Ground Penetrating Radar (GPR) consists in an electromagnetic signal which is transmitted at different positions through the ground in order to obtain an image of the subsoil. In particular, the GPR is used to detect buried objects like pipes. Their detection and localisation are intricate for three main reasons. First, the noise is important in the resulting image due to the presence of several...
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In this paper, a discussion is made on the optimization methods that can solve the low autocorrelation sidelobes problem for polyphase sequences. This paper starts with a description and a comparison of two algorithms that are commonly used in the literature: a stochastic method and a deterministic one (a gradient descent). Then, an alternative method based on the Random Walk Metropolis-Hastings algorithm...
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In this paper, a gradient descent method is used to build radar waveform sequences with good autocorrelation and/or cross-correlation. The approach we propose is based on the energy, a function that measures the sidelobe level of a sequence, and its gradient. Then, we extend and apply it to the optimization of the coherent MIMO (Multiple Input Multiple Output) ambiguity function. We suggest to look...
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2004 12th European Signal Processing Conference > 2143 - 2146
In this paper, we use the theory of generalized likelihood ratio tests (GLRT) to study the adaptive version of the asymptotical Bayesian Optimum Radar Detector (BORD) built with a covariance matrix estimate. We investigate its properties, when the noise is modelled as a non-Gaussian complex process, such as Spherically Invariant Random Process (SIRP). We derive, for appropriate covariance matrix estimates,...
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The Reed-Xiaoli (RX) is considered as the benchmark algorithm in multidimensional anomaly detection (AD). However, the RX detector performance decreases when the statistical parameters estimation is poor. This could happen when the background is non-homogeneous or the noise independence assumption is not fulfilled. For a better performance, the statistical parameters are estimated locally using a...
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2014 IEEE Geoscience and Remote Sensing Symposium > 4715 - 4718
This paper deals with the analysis of energetic non-stationary objects in already focused High-Resolution (HR) laboratory Radar imaging. Energetic non-stationary scatterers are some backscattering variations (azimuthal variations and/or frequency variations) which are linked to the target properties, for example the Radar Cross-Section (RCS). A method, based on Multi-dimensional Time-Frequency Transform...
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Recently, a general approach for high-resolution polarimetric SAR (POLSAR) data classification in heterogeneous clutter was presented, based on a statistical test of equality of covariance matrices. Here, we extend that approach by taking advantage of the Constant False Alarm Ratio (CFAR) property of the statistical test in order to improve the clustering process. We show that the CFAR property can...
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This paper proposes the new method for wet snow mapping using SAR data. It represents a modified version of the existing Nagler's mapping method, based on winter/summer image comparison, which is considered as the classic one. Instead of the existing unique threshold, a variable threshold matrix (function of the local incidence angle for each pixel) is proposed, based on dry and wet snow backscattering...
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This paper provides comprehensive analysis of the dry snow pack backscattering coefficient dependence on the density change, for various SAR sensor parameters and chosen dry snow pack parameters, characteristic for the region of French Alps. As the result, qualitative conclusions, based on applying fundamental scattering theories (Rayleigh scattering model, Quasi Crystalline Approximation, Integral...
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In many statistical signal processing applications, the quality of the estimation of parameters of interest plays an important role. We focus in this paper, on the estimation of the covariance matrix. In the classical Gaussian context, the Sample Covariance Matrix (SCM) is the most often used, since it is the Maximum Likelihood estimate. It is easy to manage and has a lot of well-known statistical...
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Reducing the number of secondary data used to estimate the Covariance Matrix (CM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. Within this framework, the Low-Rank (LR) structure of the clutter is well-known and the corresponding LR STAP filters have been shown to exhibit a smaller SNR loss than classical STAP filters, 2r secondary data (r is the clutter rank)...
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The Sample Covariance Matrix (SCM) is widely used in signal processing applications which require the estimation of the data covariance matrix. Indeed it exhibits good statistical properties and tractability. However its performance can become very bad in context of non-Gaussian signals, contaminated or missing data. In that case M-estimators provide a good alternative. They have been introduced within...
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This paper presents a new estimation scheme for optimally deriving clutter parameters with high resolution POLSAR data. The heterogeneous clutter in POLSAR data is described by the Spherically Invariant Random Vectors model. Three parameters are introduced for the high resolution POLSAR data clutter: the span, the normalized texture and the speckle normalized covariance matrix. The asymptotic distribution...
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2011 12th International Radar Symposium (IRS) > 669 - 674
We discuss in the paper the use of the Riemannian mean given by the differential geometric tools. This geometric mean is used in this paper for computing the centers of class in the polarimetric H/α unsupervised classification process. We can show that the centers of class will remain more stable during the iteration process, leading to a different interpretation of the H/α/A classification. This...
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IEEE Journal of Selected Topics in Signal Processing > 2011 > 5 > 3 > 567 - 576
This paper presents a general approach for high- resolution polarimetric SAR data classification in heterogeneous clutter, based on a statistical test of equality of covariance matrices. The Spherically Invariant Random Vector (SIRV) model is used to describe the clutter. Several distance measures, including classical ones used in standard classification methods, can be derived from the general test...
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This paper presents a new estimation scheme for optimally deriving clutter parameters with high resolution repeat-pass SAR interferometry. The heterogeneous clutter in InSAR data is described by the Spherically Invariant Random Vectors model. Three parameters are introduced for the high resolution InSAR data clutter: the span, the normalized texture and the speckle normalized covariance matrix. The...
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Previously, the well-known Optimum Gaussian Detector (OGD) has been extended to the Multiple-Input Multiple-Output (MIMO) case where all transmit-receive subarrays are considered jointly as a system such that only one detection threshold is used. In this extension, all subarrays have been assumed to be widely separated and the transmitted waveforms are assumed to be orthogonal. However, the necessary...
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IET Radar, Sonar & Navigation > 2010 > 4 > 3 > 384 - 402
Classical radar imaging generally considers targets as set of isotropic independent sources with a constant response in the measured frequency band. Nevertheless, new radar capabilities, in terms of signal bandwidth and angular excursion, may challenge this bright point model. Studies based on multidimensional time-frequency (TF) analysis, describing the angular and frequency behaviour of a scene-s...
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IEEE Transactions on Geoscience and Remote Sensing > 2010 > 48 > 4-1 > 1809 - 1826
This paper presents an application of the recent advances in the field of spherically invariant random vector (SIRV) modeling for coherency matrix estimation in heterogeneous clutter. The complete description of the polarimetric synthetic aperture radar (POLSAR) data set is achieved by estimating the span and the normalized coherency independently. The normalized coherency describes the polarimetric...
