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4 results on '"Lagrange, Adrien"'

1. A Bayesian model for joint unmixing, clustering and classification of hyperspectral data

Author

Lagrange, Adrien, Dobigeon, Nicolas, Fauvel, Mathieu, May, Stéphane, Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées, Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Centre National d'Études Spatiales [Toulouse] (CNES), Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et de Télécommunications, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Signal et Communications (IRIT-SC), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Institut National Polytechnique (Toulouse) (Toulouse INP), (OATAO), Open Archive Toulouse Archive Ouverte, and Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)

Subjects
Traitement des images, ComputingMethodologies_PATTERNRECOGNITION, Markov random field, Image interpretation, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [SDV]Life Sciences [q-bio], Bayesian model, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Super- Vised learning, [SHS]Humanities and Social Sciences
Abstract

International audience; Supervised classification and spectral unmixing are two methods to extract information from hyperspectral images. However, despite their complementarity, they have been scarcely considered jointly. This paper presents a new hierarchical Bayesian model to perform simultaneously both analysis in order to ensure that they benefit from each other. A linear mixture model is proposed to described the pixel measurements. Then a clustering is performed to identify groups of statistically similar abundance vectors. A Markov random field (MRF) is used as prior for the corresponding cluster labels. It pro-motes a spatial regularization through a Potts-Markov potential and also includes a local potential induced by the classification. Finally, the classification exploits a set of possibly corrupted labeled data provided by the end-user. Model parameters are estimated thanks to a Markov chain Monte Carlo (MCMC) algorithm. The interest of the proposed model is illustrated on synthetic and real data.

Published
2017

2. Hierarchical Bayesian image analysis: From low-level modeling to robust supervised learning.

Author

Lagrange, Adrien, Fauvel, Mathieu, May, Stéphane, and Dobigeon, Nicolas

Subjects
*HIERARCHICAL Bayes model, *IMAGE processing, *ROBUST control, *SUPERVISED learning, *PATTERN recognition systems, *HYPERSPECTRAL imaging systems
Abstract

Highlights • This paper proposes a unified framework to perform classification and low-level modeling jointly. • Robustness is improved by considering a possibly badly labeled training set. • The proposed model allows a very rich interpretation of the modeled data structure. • Performance is assessed on synthetic and real data in the specific context of hyperspectral image interpretation. • The proposed model is generic enough to incorporate any kind of low-level modeling. Abstract Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervised context. This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised information to help latent variable extraction. The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering these features into statistically homogeneous groups and a last classification stage exploiting the (possibly badly) labeled data. Performance of the model is assessed in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification. [ABSTRACT FROM AUTHOR]

Published
2019
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3. Hierarchical Bayesian image analysis: from low-level modeling to robust supervised learning

Author

Adrien Lagrange, Nicolas Dobigeon, Stéphane May, Mathieu Fauvel, Institut de Recherche en Informatique de Toulouse - École nationale supérieure d'électrotechnique, d'électronique, d'informatique, d'hydraulique et des télécommunications (IRIT - ENSEEIHT), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-ENSEEIHT, Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Dynamiques Forestières dans l'Espace Rural (DYNAFOR), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National d’Études Spatiales [Paris] (CNES), Université Fédérale Toulouse Midi-Pyrénées, Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Centre National d’Etudes Spatiales, Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Signal et Communications (IRIT-SC), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1), Lagrange, Adrien, Centre National d'Études Spatiales - CNES (FRANCE), Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Recherche Agronomique - INRA (FRANCE), Institut universitaire de France - IUF (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), and Institut National Polytechnique de Toulouse - INPT (FRANCE)

Subjects
FOS: Computer and information sciences, Computer science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer Vision and Pattern Recognition (cs.CV), [SDV]Life Sciences [q-bio], Bayesian probability, Feature extraction, Computer Science - Computer Vision and Pattern Recognition, Machine Learning (stat.ML), 02 engineering and technology, Latent variable, Bayesian inference, 01 natural sciences, supervised learning, Analyse numérique, Traitement des images, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Statistics - Machine Learning, Artificial Intelligence, 0103 physical sciences, 0202 electrical engineering, electronic engineering, information engineering, 010306 general physics, Cluster analysis, Markov random field, business.industry, Dimensionality reduction, Supervised learning, Pattern recognition, image interpretation, Markov Random Field, Informatique, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], Image interpretation, ComputingMethodologies_PATTERNRECOGNITION, Bayesian model, Signal Processing, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, Classifier (UML), Software
Abstract

International audience; Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervised context. This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised information to help latent variable extraction. The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering these features into statistically homogeneous groups and a last classification stage exploiting the (possibly badly) labeled data. Performance of the model is assessed in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification.

Published
2017

4. A Bayesian model for joint unmixing, clustering and classification of hyperspectral data

Author

Dobigeon , Nicolas, Fauvel, Mathieu, May, Stéphane, and Lagrange, Adrien

Subjects
bayesian model, markov random field, super- vised learning, image interpretation, ComputingMethodologies_PATTERNRECOGNITION
Abstract

Supervised classification and spectral unmixing are two methods to extract information from hyperspectral images. However, despite their complementarity, they have been scarcely considered jointly. This paper presents a new hierarchical Bayesian model to perform simultaneously both analysis in order to ensure that they benefit from each other. A linear mixture model is proposed to described the pixel measurements. Then a clustering is performed to identify groups of statistically similar abundance vectors. A Markov random field (MRF) is used as prior for the corresponding cluster labels. It pro-motes a spatial regularization through a Potts-Markov potential and also includes a local potential induced by the classification. Finally, the classification exploits a set of possibly corrupted labeled data provided by the end-user. Model parameters are estimated thanks to a Markov chain Monte Carlo (MCMC) algorithm. The interest of the proposed model is illustrated on synthetic and real data.

Published
2017

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