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Jean-Yves Tourneret

15 accepted papers

2025

Estimating Instrument Spectral Response Functions Using Sparse Representations and Quadratic Envelopes

Jihanne El Haouari, Marcus Carlsson, Jean-Yves Tourneret, Herwig Wendt, Jean-Michel Gaucel, Christelle Pittet
ICASSP 2025accepted

The estimation of high resolution spectrometer Instrument Spectral Response Functions (ISRFs) is crucial because an imperfect knowledge of these functions can induce errors in the measurements. The state-of-the-art for this problem currently relies on the use of parametric models, which frequently l…

Cited by 0Source ↗Scholar ↗
2020

Anomaly Detection in Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring

Barbara Pilastre, Gustavo Silva, Loïc Boussouf, Stéphane D'Escrivan, Paul Rodríguez, Jean-Yves Tourneret
ICASSP 2020accepted

This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data jointly, is intrinsically shift-invariant, and…

Cited by 0Source ↗Scholar ↗
2019

3D Reconstruction Using Single-photon Lidar Data Exploiting the Widths of the Returns

Julián Tachella, Yoann Altmann, Steve McLaughlin, Jean-Yves Tourneret
ICASSP 2019accepted

Single-photon light detection and ranging (Lidar) data can be used to capture depth and intensity profiles of a 3D scene. In a general setting, the scenes can have an unknown number of surfaces per pixel (semi-transparent surfaces or outdoor measurements), high background noise (strong ambient illum…

Cited by 0Source ↗Scholar ↗
2019

On Nonparametric Identification of Wiener Systems with Deterministic Inputs

Simone Urbano, Eric Chaumette, Philippe Goupil, Jean-Yves Tourneret
ICASSP 2019accepted

The identification of nonlinear Wiener models (NWMs) for deterministic inputs and Gaussian noise is studied. We show that the nonparametric kernel regression estimation of the nonlinearity of a NWM (based on the Nadaraya-Watson kernel estimator) can be formulated as a parametric estimation problem l…

Cited by 1Source ↗Scholar ↗
2018

On the High-Snr Receiver Operating Characteristic of Glrt for The Conditional Signal Model

Simone Urbano, Eric Chaumette, Philippe Goupil, Jean-Yves Tourneret
ICASSP 2018accepted

This paper studies the performance of the generalized likelihood ratio test (GLRT) for the conditional signal model. By conditional signal model, we mean that under both hypotheses, the observations are a linear superposition of unknown deterministic signals corrupted by additive noise, with a mixin…

Cited by 0Source ↗Scholar ↗
2017

A Bayesian lower bound for parameter estimation of Poisson data including multiple changes

Lucien Bacharach, Mohammed Nabil El Korso, Alexandre Renaux, Jean-Yves Tourneret
ICASSP 2017accepted

This paper derives lower bounds for the mean square errors of parameter estimators in the case of Poisson distributed data subjected to multiple abrupt changes. Since both change locations (discrete parameters) and parameters of the Poisson distribution (continuous parameters) are unknown, it is app…

Cited by 0Source ↗Scholar ↗
2017

Bayesian reconstruction of hyperspectral images by using compressed sensing measurements and a local structured prior

Yuri Mejia, Henry Arguello, Facundo Costa, Jean-Yves Tourneret, Hadj Batatia
ICASSP 2017accepted

This paper introduces a hierarchical Bayesian model for the reconstruction of hyperspectral images using compressed sensing measurements. This model exploits known properties of natural images, promoting the recovered image to be sparse on a selected basis and smooth in the image domain. The posteri…

Cited by 5Source ↗Scholar ↗
2016

A Bayesian framework for the multifractal analysis of images using data augmentation and a whittle approximation

Sébastien Combrexelle, Herwig Wendt, Yoann Altmann, Jean-Yves Tourneret, Steve McLaughlin, Patrice Abry
ICASSP 2016accepted

Texture analysis is an image processing task that can be conducted using the mathematical framework of multifractal analysis to study the regularity fluctuations of image intensity and the practical tools for their assessment, such as (wavelet) leaders. A recently introduced statistical model for le…

Cited by 0Source ↗Scholar ↗
2016

A maximum likelihood-based unscented Kalman filter for multipath mitigation in a multi-correlator based GNSS receiver

Cheng Cheng, Quan Pan, Vincent Calmettes, Jean-Yves Tourneret
ICASSP 2016accepted

In complex environments, the presence or absence of multipath signals not only depends on the relative motion between the GNSS receiver and navigation satellites, but also on the environment where the receiver is located. Thus it is difficult to use a specific propagation model to accurately capture…

Cited by 0Source ↗Scholar ↗
2016

Unmixing multitemporal hyperspectral images with variability: An online algorithm

Pierre-Antoine Thouvenin, Nicolas Dobigeon, Jean-Yves Tourneret
ICASSP 2016accepted

Hyperspectral unmixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal…

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2015

A Bayesian approach for the joint estimation of the multifractality parameter and integral scale based on the Whittle approximation

Sébastien Combrexelle, Herwig Wendt, Patrice Abry, Nicolas Dobigeon, Stephen McLaughlin, Jean-Yves Tourneret
ICASSP 2015accepted

Multifractal analysis is a powerful tool used in signal processing. Multifractal models are essentially characterized by two parameters, the multifractality parameter c2 and the integral scale A (the time scale beyond which multifractal properties vanish). Yet, most applications concentrate on estim…

Cited by 3Source ↗Scholar ↗
2015

A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret, Paul Honeine
ICASSP 2015accepted

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. Each image pixel is modeled by a linear combination of random endmembers to take into account endmember variability in the image. The coefficients of this linear combination…

Cited by 0Source ↗Scholar ↗
2015

Change detection for optical and radar images using a Bayesian nonparametric model coupled with a Markov random field

Jorge Prendes, Marie Chabert, Frédéric Pascal, Alain Giros, Jean-Yves Tourneret
ICASSP 2015accepted

This paper introduces a Bayesian non parametric (BNP) model associated with a Markov random field (MRF) for detecting changes between remote sensing images acquired by homogeneous or heterogeneous sensors. The proposed model is built for an analysis window which takes advantage of the spatial inform…

Cited by 34Source ↗Scholar ↗