Geoscience and Remote Sensing IEEE International Symposium, 2004
This article handles with the problem of man-made structure extraction in high resolution Synthet... more This article handles with the problem of man-made structure extraction in high resolution Synthetic Aperture Radar (SAR) data. The ability of new sensors to provide fine resolution imagery of the Earth surface leads to new remote sensing applications. As a matter of fact, the extraction and recognition of smaller and smaller structures in crowded environment is now possible: in dense
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
This paper deals with the problem of road extraction in high resolution SAR data. The presented m... more This paper deals with the problem of road extraction in high resolution SAR data. The presented method is an improvement of previous works. The process is an almost automatic algorithm based on a Hough transform and a road tracking algorithm. The main limitations of the process are due to the road context. First investigations to exploit contextual information in the
This paper addresses the problem of detecting the presence of colored multiplicative noise, when ... more This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters.
This paper addresses the problem of changepoint detection in FARIMA processes. The received signa... more This paper addresses the problem of changepoint detection in FARIMA processes. The received signal is modeled as a FARIMA process, with abrupt changes in the Hurst and ARMA parameters. The proposed changepoint detection method first estimates the model parameters over small segments. The changes are then detected in the parameter vector sequence by minimizing an appropriate least-squares criterion. The cases of known, as well as unknown, number of changes are investigated. Dynamic programming is used to solve this optimization problem. A theoretical analysis of the statistical properties of the changepoint estimates is provided. Simulation results on synthetic data and real network traffic data are presented.
ABSTRACT Cet article propose une nouvelle méthode de démélange aveugle d'images hyper... more ABSTRACT Cet article propose une nouvelle méthode de démélange aveugle d'images hyperspectrales pour estimer conjointement les spectres des composants purs de l'image et leurs proportions respectives au sein de chaque pixel. Cet algorithme totalement bayésien repose sur le choix judicieux de lois a priori permettant de respecter les contraintes inhérentes au modèle d'observation, et sur l'estimation des signatures spectrales dans un sous-espace approprié. Cette estimation conjointe permet de pallier les inconvénients des méthodes de démélange spectral basées sur deux étapes successives. Elle fournit également des estimations des paramètres d'intérêt qui respectent toutes les contraintes de positivité et d'additivité caractéristiques du modèle. Des simulations conduites sur des images synthétiques et réelles permettent d'illustrer les performances de la méthode proposée.
This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation f... more This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.
Geoscience and Remote Sensing IEEE International Symposium, 2004
This article handles with the problem of man-made structure extraction in high resolution Synthet... more This article handles with the problem of man-made structure extraction in high resolution Synthetic Aperture Radar (SAR) data. The ability of new sensors to provide fine resolution imagery of the Earth surface leads to new remote sensing applications. As a matter of fact, the extraction and recognition of smaller and smaller structures in crowded environment is now possible: in dense
Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., 2005
This paper deals with the problem of road extraction in high resolution SAR data. The presented m... more This paper deals with the problem of road extraction in high resolution SAR data. The presented method is an improvement of previous works. The process is an almost automatic algorithm based on a Hough transform and a road tracking algorithm. The main limitations of the process are due to the road context. First investigations to exploit contextual information in the
This paper addresses the problem of detecting the presence of colored multiplicative noise, when ... more This paper addresses the problem of detecting the presence of colored multiplicative noise, when the information process can be modeled as a parametric ARMA process. For the case of zero-mean multiplicative noise, a cumulant based suboptimal detector is studied. This detector tests the nullity of a specific cumulant slice. A second detector is developed when the multiplicative noise is nonzero mean. This detector consists of filtering the data by an estimated AR filter. Cumulants of the residual data are then shown to be well suited to the detection problem. Theoretical expressions for the asymptotic probability of detection are given. Simulation-derived finite-sample ROC curves are shown for different sets of model parameters.
This paper addresses the problem of changepoint detection in FARIMA processes. The received signa... more This paper addresses the problem of changepoint detection in FARIMA processes. The received signal is modeled as a FARIMA process, with abrupt changes in the Hurst and ARMA parameters. The proposed changepoint detection method first estimates the model parameters over small segments. The changes are then detected in the parameter vector sequence by minimizing an appropriate least-squares criterion. The cases of known, as well as unknown, number of changes are investigated. Dynamic programming is used to solve this optimization problem. A theoretical analysis of the statistical properties of the changepoint estimates is provided. Simulation results on synthetic data and real network traffic data are presented.
ABSTRACT Cet article propose une nouvelle méthode de démélange aveugle d'images hyper... more ABSTRACT Cet article propose une nouvelle méthode de démélange aveugle d'images hyperspectrales pour estimer conjointement les spectres des composants purs de l'image et leurs proportions respectives au sein de chaque pixel. Cet algorithme totalement bayésien repose sur le choix judicieux de lois a priori permettant de respecter les contraintes inhérentes au modèle d'observation, et sur l'estimation des signatures spectrales dans un sous-espace approprié. Cette estimation conjointe permet de pallier les inconvénients des méthodes de démélange spectral basées sur deux étapes successives. Elle fournit également des estimations des paramètres d'intérêt qui respectent toutes les contraintes de positivité et d'additivité caractéristiques du modèle. Des simulations conduites sur des images synthétiques et réelles permettent d'illustrer les performances de la méthode proposée.
This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation f... more This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.
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