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Jean Philippe Ovarlez

16 accepted papers

2025

Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders

Yadang Alexis Rouzoumka, Eugénie Terreaux, Christèle Morisseau, Jean Philippe Ovarlez, Chengfang Ren
ICASSP 2025accepted

This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-of-distribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, inclu…

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2023

False Alarm Regulation for Off-Grid Target Detection With The Matched Filter

Pierre Develter, Jonathan Bosse, Olivier Rabaste, Philippe Forster, Jean Philippe Ovarlez
ICASSP 2023accepted

In the state-of-the-art, the Probability of False Alarm (P<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FA</inf>)- threshold relationship for the popular Matched Filter (MF) is often derived assuming that unknown non-linear parameters lie on a grid. H…

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2023

Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification

Cyprien Doz, Chengfang Ren, Jean Philippe Ovarlez, Romain Couillet
ICASSP 2023accepted

This article analyzes a kernel-based transfer learning method, under a k-class Gaussian mixture model for the input data. Following recent advances in random matrix theory, we propose new insights in transfer learning schemes for challenging cases, when the first-order statistics of all data classes…

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2022

On the False Alarm Probability of the Normalized Matched Filter for Off-Grid Target Detection

Pierre Develter, Jonathan Bosse, Olivier Rabaste, Philippe Forster, Jean Philippe Ovarlez
ICASSP 2022accepted

Off-grid targets are known to induce a mismatch that dramatically impacts the detection probability of the popular Normalized Matched Filter. To overcome this problem, the unknown target parameter is usually estimated through a Maximum Likelihood strategy resulting in a GLRT detection scheme. While…

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2022

On the Use of Geodesic Triangles between Gaussian Distributions for Classification Problems

Antoine Collas, Florent Bouchard, Guillaume Ginolhac, Arnaud Breloy, Chengfang Ren, Jean Philippe Ovarlez
ICASSP 2022accepted

This paper presents a new classification framework for both first and second order statistics, i.e. mean/location and covariance matrix. In the last decade, several covariance matrix classification algorithms have been proposed. They often leverage the Riemannian geometry of symmetric positive defin…

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2021

A Tyler-Type Estimator of Location and Scatter Leveraging Riemannian Optimization

Antoine Collas, Florent Bouchard, Arnaud Breloy, Chenfang Ren, Guillaume Ginolhac, Jean Philippe Ovarlez
ICASSP 2021accepted

We consider the problem of jointly estimating the location and scatter matrix of a Compound Gaussian distribution with unknown deterministic texture parameters. When the location is known, the Maximum Likelihood Estimator (MLE) of the scatter matrix corresponds to Tyler’s M-estimator, which can be c…

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2021

Complex-Valued Vs. Real-Valued Neural Networks for Classification Perspectives: An Example on Non-Circular Data

Jose Agustin Barrachina, Chenfang Ren, Christèle Morisseau, Gilles Vieillard, Jean Philippe Ovarlez
ICASSP 2021accepted

This paper shows the benefits of using Complex-Valued Neural Network (CVNN) on classification tasks for non-circular complex-valued datasets. Motivated by radar and especially Synthetic Aperture Radar (SAR) applications, we propose a statistical analysis of fully connected feed-forward neural networ…

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2020

Robust Covariance Matrix Estimation and Portfolio Allocation: The Case of Non-Homogeneous Assets

Emmanuelle Jay, Thibault Soler, Jean Philippe Ovarlez, Philippe de Peretti, Christophe Chorro
ICASSP 2020accepted

This paper presents how the most recent improvements made on covariance matrix estimation and model order selection can be applied to the portfolio optimization problem. Our study is based on the case of the Maximum Variety Portfolio and may be obviously extended to other classical frameworks with a…

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2019

Designing Sar Images Change-point Estimation Strategies Using an Mse Lower Bound

Ammar Mian, Lucien Bacharach, Guillaume Ginolhac, Alexandre Renaux, Mohammed Nabil El Korso, Jean Philippe Ovarlez
ICASSP 2019accepted

A growing problem in the remote sensing community concerns the estimation of change-points in a time series of Synthetic Aperture Radar (SAR) images. Although the methodologies of change-point estimation have already been investigated in the literature, there are, to the best of our knowledge, no st…

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2018

A Robust Change Detector for Highly Heterogeneous Multivariate Images

Ammar Mian, Jean Philippe Ovarlez, Guillaume Ginolhac, Abdourrahmane M. Atto
ICASSP 2018accepted

In this paper, we propose new detectors for Change Detection between two multivariate images. The data is supposed to fol-Iowa Compound Gaussian distribution. By using Likelihood Ratio Test (LRT) and Generalised LRT (GLRT) approaches, we derive our detectors. The CFAR behaviour has been studied and…

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2018

A Toeplitz-Tyler Estimation of the Model Order in Large Dimensional Regime

Eugénie Terreaux, Jean Philippe Ovarlez, Frédéric Pascal
ICASSP 2018accepted

This paper presents a new algorithm to estimate the number of sources embedded in a correlated Complex Elliptically Distributed (CES) noise in the context of large dimensional regime. The proposed method is a two-steps ones: first the data covariance matrix is estimated with a robust and consistent…

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2018

Target and Background Separation in Hyperspectral Imagery for Automatic Target Detection

Ahmad W. Bitar, Loong-Fah Cheong, Jean Philippe Ovarlez
ICASSP 2018accepted

In this paper, we propose a method for separating known 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 known targets base…

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2017

Multivariate Linear Time-Frequency modeling and adaptive robust target detection in highly textured monovariate SAR image

Jean Philippe Ovarlez, Guillaume Ginolhac, Abdourrahmane M. Atto
ICASSP 2017accepted

Usually, in radar imaging, the scatterers are supposed to respond the same way regardless of the angle from which they are viewed and have the same properties within the emitted spectral bandwidth. Nevertheless, new capacities in SAR imaging (large bandwidth, large angular extent) make this assumpti…

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2017

Simultaneous sparsity-based binary hypothesis model for real hyperspectral target detection

Ahmad W. Bitar, Loong-Fah Cheong, Jean Philippe Ovarlez
ICASSP 2017accepted

In this paper, a simultaneous sparsity representation-based binary hypothesis (S-SRBBH) model for target detection in hyperspectral image (HSI) is proposed. The S-SRBBH exploits the interpixel correlation within neighboring pixels in HSI, and then, each test pixel is represented by only the backgrou…

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