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7 results on '"ANR-10-COSI-0012,FOSTER(2010)"'

1. Satellite Image Time Series: Mathematical Models for Data Mining and Missing Data Restoration

Author

Farid Melgani, Nicolas Méger, Edoardo Pasolli, Christophe Rigotti, Emmanuel Trouvé, Laboratoire d'Informatique, Systèmes, Traitement de l'Information et de la Connaissance (LISTIC), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), University of Trento [Trento], Artificial Evolution and Computational Biology (BEAGLE), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Data Mining and Machine Learning (DM2L), Université de Lyon-Université Lumière - Lyon 2 (UL2), Department of Information engineering and computer science (DISI), Università degli Studi di Trento (UNITN), Gabriele Moser, Josiane Zerubia, ANR-07-MDCO-0004,EFIDIR,Extraction et Fusion d'Informations pour la mesure de Déplacement par Imagerie Radar(2007), ANR-10-COSI-0012,FOSTER(2010), Méger, Nicola, Pasolli, Edoardo, Rigotti, Christophe, Trouvé, Emmanuel, Melgani, Farid, Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Series (mathematics), Multispectral image, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, 02 engineering and technology, Iterative reconstruction, Missing data, computer.software_genre, Displacement (vector), Geography, Compressed sensing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Interferometric synthetic aperture radar, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Satellite Image Time Series, Data mining, computer, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering
Abstract

One of the exceptional advantages of spaceborne remote sensors is their regular scanning of the Earth surface, resulting thus in Satellite Image Time Series (SITS), extremely useful to monitor natural or man-made phenomena on the ground. In this chapter, after providing a brief overview of the most recent methods proposed to process and/or analyze time series of remotely sensed data, we describe methods handling two issues: the unsupervised exploration of SITS and the reconstruction of multispectral images. In particular, we first present data mining methods for extracting spatiotemporal patterns in an unsupervised way and illustrate this approach on time series of displacement measurements derived from multitemporal InSAR images. Then we present two methods which aim to reconstruct multispectral images contaminated by the presence of clouds. The first one is based on a linear contextual prediction mode that reproduces the local spectro-temporal relationships characterizing a given time series of images. The second method tackles the image reconstruction problem within a compressive sensing formulation and with different implementation strategies. A rich set of illustrations on real and simulated examples is provided and discussed.

Published
2017

2. Attributed graph mining in the presence of automorphism

Author

Jérémy Sanhes, Claude Pasquier, Frédéric Flouvat, Nazha Selmaoui-Folcher, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME), Université de la Nouvelle-Calédonie (UNC), and ANR-10-COSI-0012,FOSTER(2010)

Subjects
Molecule mining, Discrete mathematics, 02 engineering and technology, Directed graph, Structure mining, Automorphism, Graph, Human-Computer Interaction, Combinatorics, Artificial Intelligence, Hardware and Architecture, 020204 information systems, Attributed graph Frequent pattern mining Automorphism Structure mining Itemset mining, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Software, Combinatorial explosion, Computer Science::Databases, Information Systems, Mathematics, MathematicsofComputing_DISCRETEMATHEMATICS
Abstract

International audience; Attributed directed graphs are directed graphs in which nodes are associated with sets of attributes. Many data from the real world can be naturally represented by this type of structure, but few algorithms are able to directly handle these complex graphs. Mining attributed graphs is a difficult task because it requires combining the exploration of the graph structure with the identification of frequent itemsets. In addition, due to the combinatorics on itemsets, subgraph isomorphisms (which have a significant impact on performances) are much more numerous than in labeled graphs. In this paper, we present a new data mining method that can extract frequent patterns from one or more directed attributed graphs. We show how to reduce the combinatorial explosion induced by subgraph isomorphisms thanks to an appropriate processing of automorphic patterns.

Published
2017

3. Frequent Pattern Mining in Attributed Trees: algorithms and applications

Author

Nazha Selmaoui-Folcher, Frédéric Flouvat, Jérémy Sanhes, Claude Pasquier, Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Pôle Pluridisciplinaire de la Matière et de l'Environnement (PPME), Université de la Nouvelle-Calédonie (UNC), and ANR-10-COSI-0012,FOSTER(2010)

Subjects
Closure (topology), Tree mining, 02 engineering and technology, computer.software_genre, Execution time, Domain (software engineering), Artificial Intelligence, 020204 information systems, Frequent pattern mining, 0202 electrical engineering, electronic engineering, information engineering, Mathematics, Structure (mathematical logic), Attributed tree, Condensed representation, InformationSystems_DATABASEMANAGEMENT, Human-Computer Interaction, Task (computing), Range (mathematics), ComputingMethodologies_PATTERNRECOGNITION, Hardware and Architecture, Scalability, 020201 artificial intelligence & image processing, Data mining, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], Algorithm, computer, Software, Information Systems
Abstract

International audience; Frequent pattern mining is an important data mining task with a broad range of applications. Initially focused on the discovery of frequent itemsets, studies were extended to mine structural forms like sequences, trees or graphs. In this paper, we introduce a new domain of patterns, attributed trees (atrees), and a method to extract these patterns in a forest of atrees. Attributed trees are trees in which vertices are associated with itemsets. Mining this type of patterns (called asubtrees), which combines tree mining and itemset mining, requires the exploration of a huge search space. To make our approach scalable, we investigate the mining of condensed representations. For attributed trees, the classical concept of closure involves both itemset closure and structural closure. We present three algorithms for mining all patterns, closed patterns w.r.t. itemsets (content) and/or structure in attributed trees. We show that, for low support values, mining content-closed attributed trees is a good compromise between non-redundancy of solutions and execution time.

Published
2015

4. Automated detection of landslides with a hierarchical multi-resolution image analysis approach

Author

Kurtz, Camille, Stumpf, André, Malet, Jean-Philippe, Puissant, Anne, Gançarski, Pierre, Passat, Nicolas, Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517), Université Paris Descartes - Paris 5 (UPD5), Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Institut de physique du globe de Strasbourg (IPGS), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Les Hôpitaux Universitaires de Strasbourg (HUS)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), ANR-10-COSI-0012,FOSTER(2010), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université de Brest (UBO), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Matériaux et nanosciences d'Alsace, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Matériaux et nanosciences d'Alsace (FMNGE), and Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology
Abstract

International audience; The mapping of landslides from Very High Resolution (VHR) satellite optical images present several challenges related to the heterogeneity of landslide sizes, shapes and ground surface properties. However, a common geomorphological characteristic of landslides is to be organized with a series of embedded and scaled features. These properties motivated the use of a multiresolution image analysis approach based on a hybrid segmentation/classification region-based method. The method, which uses satellite optical images of the same area at various spatial resolutions (Medium to Very High Resolution), relies on a top-down hierarchical framework. In the specific context of landslide analysis, two main novelties are introduced to enrich this framework. The first novelty consists of using non-spectral information, obtained from Digital Surface Model (DSM), as a priori knowledge for the guidance of the segmentation/classification process. The second novelty consists of using a new domain adaptation strategy, that allows to reduce the expert’s interaction when handling large image datasets. Experiments performed on satellite images acquired over terrains affected by landslides in ther French Alps demonstrate the efficiency of the proposed method with different hierarchical levels of detail addressing various operational needs.

Published
2015

5. Domain adaptation for the extraction of complex urban patterns from multiresolution satellite images

Author

Anne Puissant, Nicolas Passat, Camille Kurtz, Pierre Gançarski, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Centre National de la Recherche Scientifique (CNRS), Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), and ANR-10-COSI-0012,FOSTER(2010)

Subjects
Computer science, hierarchical segmentation, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, 0211 other engineering and technologies, Multiresolution satellite images, Scale-space segmentation, Context (language use), 02 engineering and technology, Clustering, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Urban analysis, 0202 electrical engineering, electronic engineering, information engineering, Segmentation, Computer vision, Cluster analysis, 021101 geological & geomatics engineering, Domain adaptation, Contextual image classification, business.industry, Segmentation-based object categorization, Process (computing), [SHS.GEO]Humanities and Social Sciences/Geography, Image segmentation, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], 020201 artificial intelligence & image processing, Artificial intelligence, business
Abstract

International audience; The extraction of complex urban patterns from Very High Spatial Resolution (VHSR) images presents several challenges related to the complexity of the data. Based on the availability of images of a same scene at various resolutions (Medium to Very High Spatial resolutions), a hierarchical approach has been recently proposed to segment/classify objects of interest in a top-down fashion in order to determine patterns from VHSR images. To perform, this method requires the interactive definition of segmentation examples for each considered resolution image. In the context of large dataset processing, such interactive task becomes time consuming. To deal with this issue, we propose in this article, an extension of the domain adaptation paradigm enabling the transfer of the segmenta tion examples defined on a source dataset to automatically process a target one. Experiments performed on urban images provide satisfactory results which may be further used for operational needs.

Published
2012

6. Morphologie urbaine et classification d'images orientées-objets

Author

Puissant, Anne, Kurtz, Camille, Passat, Nicolas, Gançarski, Pierre, Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Centre National de la Recherche Scientifique (CNRS), ANR-10-COSI-0012,FOSTER(2010), Passat, Nicolas, and CONCEPTION ET SIMULATION - - FOSTER2010 - ANR-10-COSI-0012 - COSINUS - VALID

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], classification, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SHS.GEO] Humanities and Social Sciences/Geography, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Morphologie urbaine, [SHS.GEO]Humanities and Social Sciences/Geography, ComputingMilieux_MISCELLANEOUS, images orientées-objets
Abstract

International audience

Published
2011

7. Observation multi-capteurs d'objets environnementaux

Author

Puissant, Anne, Weber, Christiane, Serradj, Aziz, Skupinski, Grzegorz, Malet, Jean-Philippe, Travelletti, Julien, Gançarski, Pierre, Passat, Nicolas, Passat, Nicolas, CONCEPTION ET SIMULATION - - FOSTER2010 - ANR-10-COSI-0012 - COSINUS - VALID, Laboratoire Image, Ville, Environnement (LIVE), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut de physique du globe de Strasbourg (IPGS), Université de Strasbourg (UNISTRA)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), School and Observatory of Earth Sciences, Institute of Earth physics, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection (LSIIT), Centre National de la Recherche Scientifique (CNRS), and ANR-10-COSI-0012,FOSTER(2010)

Subjects
[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], [SHS.GEO] Humanities and Social Sciences/Geography, [SHS.GEO]Humanities and Social Sciences/Geography, ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2011

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