Jean-Philippe Ovarlez

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Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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29 March 2019 in IEEE Transactions on Geoscience and Remote Sensing

Journal Article

Sparse and Low-Rank Matrix Decomposition for Automatic Target Detection in Hyperspectral Imagery

Ahmad W. Bitar

Loong-Fah Cheong

Jean-Philippe Ovarlez

Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery. More precisely, we regard the given hyperspectral image (HSI) as being made up of the sum of low-rank background HSI and a sparse target HSI that contains the targets based on a prelearned target dictionary constructed from some online spectral libraries. Based on the proposed method, two strategies are briefly outlined and evaluated to realize the target detection on both synthetic and real experiments.

29 November 2018 in IEEE Transactions on Signal Processing

Journal Article

New Robust Statistics for Change Detection in Time Series of Multivariate SAR Images

Ammar Mian

Guillaume Ginolhac

Jean-Philippe Ovarlez

Abdourrahmane Atto

This paper explores the problem of change detection in time series of heterogeneous multivariate synthetic aperture radar images. Classical change detection schemes have modelled the data as a realisation of Gaussian random vectors and have derived statistical tests under this assumption. However, when considering high-resolution images, the heterogeneous behaviour of the scatterers is not well described by a Gaussian model. In this paper, the data model is extended to Spherically Invariant Random Vectors where the heterogeneity of the images is accounted for through a deterministic texture parameter. Then three separate detection problems are considered and generalised likelihood ratio test technique is used to derive statistical tests for each problem. The constant false alarm rate property of the new statistics are studied both theoretically and through simulation. Finally, the performance of the new statistics are studied both in simulation and on real synthetic aperture radar data and compared to Gaussian-derived ones. The study yields promising results when the data are heterogeneous.

26 November 2021 in IEEE Transactions on Signal Processing

Journal Article

Probabilistic PCA From Heteroscedastic Signals: Geometric Framework and Application to Clustering

Jean-Philippe Ovarlez

This paper studies a statistical model for heteroscedastic ( i.e. , power fluctuating) signals embedded in white Gaussian noise. Using the Riemannian geometry theory, we propose an unified approach to tackle several problems related to this model. The first axis of contribution concerns parameters (signal subspace and power factors) estimation, for which we derive intrinsic Cramér-Rao bounds and propose a flexible Riemannian optimization algorithmic framework in order to compute the maximum likelihood estimator (as well as other cost functions involving the parameters). Interestingly, the obtained bounds are in closed forms and interpretable in terms of problem’s dimensions and SNR. The second axis of contribution concerns the problem of clustering data assuming a mixture of heteroscedastic signals model, for which we generalize the Euclidean K-means++ to the considered Riemannian parameter space. We propose an application of the resulting clustering algorithm on the Indian Pines segmentation problem benchmark.

14 January 2019 in IEEE Transactions on Geoscience and Remote Sensing

Journal Article

Design of New Wavelet Packets Adapted to High-Resolution SAR Images With an Application to Target Detection

Ammar Mian

Jean-Philippe Ovarlez

Abdourrahmane Atto

Guillaume Ginolhac

High resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single-look SAR images. Modeling this behavior as nonstationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parameterized wavelets, designed specifically to reduce those side lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectro-angular diversity and it can be shown that in high-resolution SAR images, the non-Gaussian and robust framework leads to better results.

28 July 2022 in Journal of Signal Processing Systems

Journal Article

Comparison Between Equivalent Architectures of Complex-valued and Real-valued Neural Networks - Application on Polarimetric SAR Image Segmentation

José Agustín Barrachina

Chengfang Ren

Christèle Morisseau

Gilles Vieillard

Jean-Philippe Ovarlez

16 October 2017 in IEEE Signal Processing Letters

Journal Article

Robust ANMF Detection in Noncentered Impulsive Background

Joana Frontera-Pons

Jean-Philippe Ovarlez

Frederic Pascal

One of the most general and acknowledged models for background statistics characterization is the family of elliptically symmetric distributions. They account for heterogeneity and non-Gaussianity of real data. Today, although nonGaussian models are assumed for background modeling and design of detectors, the parameters estimation is usually performed using classical Gaussian-based estimators. This letter analyzes robust estimation techniques in a nonGaussian environment and highlights their interest as an alternative to classical procedures for target detection purposes. The goal of this letter is to extend well-known detection methodologies to nonGaussian framework, when the statistical mean is nonnull and unknown. Furthermore, a theoretical closed-form expression for false-alarm regulation is derived and the Constant False Alarm Rate property is pursued to allow the detector to be independent of nuisance parameters. The experimental validation is conducted on simulations.

About Me

Jean-Philippe Ovarlez was born in Denain, France in 1963. He received jointly the engineering degree from Ecole Supérieure d'Electronique Automatique et Informatique (ESIEA), Paris, France and the Diplôme d'Etudes Approfondies degree in Signal Processing from University of Paris XI, Orsay, France and the Ph.D. degree in Physics from the University of Paris VI, Paris, France, in 1987 and 1992, respectively. In 2011, he obtained a Research Directorship Habilitation (HDR) thesis in Signal Processing from the University of Paris-Sud and his qualification to the University Professor position. In 1992, he joined the Electromagnetic and Radar Division of the French Aerospace Lab (ONERA), Palaiseau, France, where he is currently Chief Scientist and member of the Scientific Committee of the ONERA Physics Branch. Since 2008, he is attached at part-time to CentraleSupélec SONDRA Lab, in charge of Signal Processing activities supervision. In 2015, he becomes a member of the Special Area Team (SAT) in Theoretical and Methodological Trends in Signal Processing (TMTSP), EURASIP, and treasurer of the IEEE GRSS French Chapter in 2016. His research interests are centered on the topic of Statistical Signal Processing for radar and SAR applications such as Time-Frequency, imaging, detection and parameters estimation.

Jean-Philippe Ovarlez

Jean-Philippe Ovarlez

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