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Fast estimation for robust supervised classification with mixture models / Erwan Giry Fouquet in Pattern recognition letters, vol 152 (December 2021)  

Public [article]  inPattern recognition letters > vol 152 (December 2021) . - pp 320 - 326 Titre : Fast estimation for robust supervised classification with mixture models Type de document : Article/Communication Auteurs : Erwan Giry Fouquet, Auteur ; Mathieu Fauvel, Auteur ; Clément Mallet , Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : MAESTRIA / Mallet, Clément, ANITI / Mallet, Clément Article en page(s) : pp 320 - 326 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] échantillon [Termes IGN] méthode robuste [Termes IGN] optimisation (mathématiques) Résumé : (auteur) Label noise is known to negatively impact the performance of classification algorithms. In this paper, we develop a model robust to label noise that uses both labelled and unlabelled samples. In particular, we propose a novel algorithm to optimize the model parameters that scales efficiently w.r.t. the number of training samples. Our contribution relies on a consensus formulation of the original objective function that is highly parallelizable. The optimization is performed with the Alternating Direction Method of Multipliers framework. Experimental results on synthetic datasets show an improvement of several orders of magnitude in terms of processing time, with no loss in terms of accuracy. Our method appears also tailored to handle real data with significant label noise. Numéro de notice : A2021-061 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.patrec.2021.10.020 Date de publication en ligne : 26/10/2021 En ligne : https://doi.org/10.1016/j.patrec.2021.10.020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99531 [article]

Time-series analysis of massive satellite images : Application to earth observation / Alexandre Constantin (2021)  

Public Titre : Time-series analysis of massive satellite images : Application to earth observation Titre original : Analyse de séries temporelles massives d'images satellitaires : Applications à la cartographie des écosystèmes Type de document : Thèse/HDR Auteurs : Alexandre Constantin, Auteur ; Stéphane Girard, Directeur de thèse ; Mathieu Fauvel, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 136 p. Format : 21 x 30 cm Note générale : bibliographie Thèse Pour obtenir le grade de Docteur de l'Université de Grenoble Alpes, Specialité : Mathématiques Appliquées Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multivariée [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] classification pixellaire [Termes IGN] covariance [Termes IGN] échantillonnage de données [Termes IGN] écosystème [Termes IGN] image Sentinel-MSI [Termes IGN] processus gaussien [Termes IGN] Python (langage de programmation) [Termes IGN] régression [Termes IGN] série temporelle Index. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis takes place in the context of the processing of the data from Sentinel-2 mission. This mission, initiated by the European Space Agency and launched in 2017, produces an unprecedented amount of Satellite Image Time-Series (SITS). Among the key analyses of these images, this thesis focuses on the classification task, i.e. land use or land cover maps that can be produced using spectro-temporal aspect of the Sentinel-2 SITS.Two main difficulties are identified in this thesis for the process of Sentinel-2 SITS. First, the unprecedented amount of data requires both scalable classifiers and code optimization techniques (such as parallel processing). Second, the acquisition noise (clouds, shadows) combined with the temporal aspect results in irregular and unevenly sampled time-series. Conventional approaches re-sample time-series to a set of time stamps, then they use machine learning techniques to classify vectors at a large-scale (national scale). The main disadvantage of this two-step processing approach is that it increases the number of operations applied to the SITS, implying a more difficult transition to massive amount of data. To a lower extent, the re-sampling step may slightly alter the temporal characteristics of the data.This thesis contributions are the following. We introduce a novel model-based approach with the ability to classify irregularly sampled time-series based on a mixture of multivariate Gaussian processes. A two-step approach has been used, by defining on one hand a model of uni-variate time-series, independent from the spectral wavelength point of view, then by considering on the second hand both spectral and temporal information from SITS. These models allow jointly a reconstruction of unobserved or noisy data. Estimation of both models has been implemented using a parallelized python code to be scalable to large-scale data-sets. The two models are evaluated numerically on Sentinel-2 SITS in terms of classification and reconstruction accuracy and are compared with conventional approaches. Analyses of the results illustrate the relevance of the two models and the benefit of using interpretable parametric models. Note de contenu : General Introduction 1- Satellite image time-series analysis and classification 2- Statistical modelling for time-series classification 3- Model-based classification for irregularly sampled time-series 4- Joint supervised classification and reconstruction of irregularly sampled satellite image times series 5- Mixture of multivariate gaussian processes for classification of irregularly sampled SITS Conclusion and perspectives Numéro de notice : 15280 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Grenoble : 2021 Organisme de stage : Laboratoire Jean Kuntzmann DOI : sans En ligne : https://hal.science/tel-03682025 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101161

Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)  

Public [article]  inRemote sensing of environment > Vol 237 (February 2020) . - 13 p. Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Projets : SEBIOREF / Ouin, Annie Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] biodiversité végétale [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] Haute-Garonne (31) [Termes IGN] image radar moirée [Termes IGN] image RapidEye [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] indice de diversité [Termes IGN] indice de végétation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] prairie [Termes IGN] série temporelle [Termes IGN] taxinomie Résumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 Date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296 [article]

International workshop on large scale land cover mapping from remote sensing, 3 décembre 2019 / Mathieu Fauvel (2019)

Public Titre : International workshop on large scale land cover mapping from remote sensing, 3 décembre 2019 Type de document : Actes de congrès Auteurs : Mathieu Fauvel, Organisateur de réunion ; Jordi Inglada, Organisateur de réunion ; Arnaud Le Bris , Organisateur de réunion ; Clément Mallet , Organisateur de réunion Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : MAESTRIA / Mallet, Clément Conférence : International workshop 2019 on large scale land cover mapping from remote sensing 03/12/2019 03/12/2019 Saint-Mandé France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] base de données d'occupation du sol [Termes IGN] image numérique [Termes IGN] occupation du sol [Termes IGN] territoire Résumé : Workshop sans actes organisé dans le cadre du projet MAESTRIA Numéro de notice : 14366 Affiliation des auteurs : LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96917

Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach / Josselin Aval in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 146 (December 2018) . - pp 197 - 210 Titre : Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach Type de document : Article/Communication Auteurs : Josselin Aval, Auteur ; Jean Demuynck, Auteur ; Emmanuel Zenou, Auteur ; Sophie Fabre, Auteur ; David Sheeren , Auteur ; Mathieu Fauvel, Auteur ; Karine R.M. Adeline, Auteur ; Xavier Briottet , Auteur Année de publication : 2018 Article en page(s) : pp 197 - 210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] arbre urbain [Termes IGN] canopée [Termes IGN] détection d'arbres [Termes IGN] données lidar [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] image hyperspectrale [Termes IGN] modèle numérique de surface [Termes IGN] prise en compte du contexte [Termes IGN] processus ponctuel marqué [Termes IGN] système d'information géographique [Termes IGN] Toulouse [Termes IGN] zone urbaine Résumé : (Auteur) With the current expansion of cities, urban trees have an important role for preserving the health of its inhabitants. With their evapotranspiration, they reduce the urban heat island phenomenon, by trapping CO2 emission, improve air quality. In particular, street trees or alignment trees, create shade on the road network, are structuring elements of the cities and decorate the roads. Street trees are also subject to specific conditions as they have little space for growth, are pruned and can be affected by the spread of diseases in single-species plantations. Thus, their detection, identification and monitoring are necessary. In this study, an approach is proposed for mapping these trees that are characteristic of the urban environment. Three areas of the city of Toulouse in the south of France are studied. Airborne hyperspectral data and a Digital Surface Model (DSM) for high vegetation detection are used. Then, contextual information is used to identify the street trees. Indeed, Geographic Information System (GIS) data are considered to detect the vegetation canopies close to the streets. Afterwards, individual street tree crown delineation is carried out by modeling the discriminative contextual features of individual street trees (hypotheses of small angle between the trees and similar heights) based on Marked Point Process (MPP). Compared to a baseline individual tree crown delineation method based on region growing, our method logically provides the best results with F-score values of 91%, 75% and 85% against 70%, 41% and 20% for the three studied areas respectively. Our approach mainly succeeds in identifying the street trees. In addition, the contribution of the angle, the height and the GIS data in the street tree mapping has been studied. The results encourage the use of the angle, the height and the GIS data together. However, with only the angle and the height, the results are similar to those obtained with the inclusion of the GIS data for the first and the second study cases with F-score values of 88%, 79% and 62% against 91%, 75% and 85% for the three study cases respectively. Finally, it is shown that the GIS data only is not sufficient. Numéro de notice : A2018-538 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.09.016 Date de publication en ligne : 21/10/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91552 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018131	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018133	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018132	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Editorial / Clément Mallet in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)  

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Suivi écologique des prairies semi-naturelles : analyse statistique de séries temporelles denses d’images satellite à haute résolution spatiale / Maylis Lopes (2018) 

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Fast forward feature selection of hyperspectral images for classification with gaussian mixture models / Mathieu Fauvel in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8 n° 6 (June 2015)  

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Vectorisation automatique des forêts dans les minutes de la carte d’état-major du 19e siècle / Pierre-Alexis Herrault in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)  

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Mission d'acquisition aérienne lidar pour les applications thématiques en écologie du paysage : le cas des coteaux de Gascogne / Sylvie Ladet in Collection EDYTEM. cahiers de géographie, n° 12 (01/06/2011)  

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