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Un algorithme pour battre le record du SwissTrainChallenge : poser le pied dans chacun des 26 cantons le plus rapidement possible en utilisant uniquement des transports publics / Emmanuel Clédat in XYZ, n° 157 (décembre 2018 - février 2019)
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Titre : Un algorithme pour battre le record du SwissTrainChallenge : poser le pied dans chacun des 26 cantons le plus rapidement possible en utilisant uniquement des transports publics Type de document : Article/Communication Auteurs : Emmanuel Clédat , Auteur ; Dirk Lauinger, Auteur Année de publication : 2018 Article en page(s) : pp 30 - 36 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] calcul d'itinéraire
[Termes IGN] chemin le plus rapide, algorithme du
[Termes IGN] connexité (graphes)
[Termes IGN] durée de trajet
[Termes IGN] multilatération
[Termes IGN] programmation linéaire
[Termes IGN] réseau ferroviaire
[Termes IGN] Suisse
[Termes IGN] train
[Termes IGN] transport public
[Termes IGN] vitesseRésumé : (auteur) The Swiss Train Challenge is to set foot in all 26 cantons of Switzerland in as little time as possible, using only public transportation. Relying on human intuition informed by a geographical information system to select the relevant train stations, and on computational power to solve the resulting mixed-integer linear optimization problem, we find a solution that beats the current record of 17 hours and 19 minutes, by 25 minutes. When testing our itinerary in practice, we made all connections but one for which the arriving train was 20 minutes delayed - a rare event in switzerland. This is the first time that an algorithm has been used to calculate the Swiss Train Challenge itinerary. Numéro de notice : A2018-532 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91577
in XYZ > n° 157 (décembre 2018 - février 2019) . - pp 30 - 36[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 112-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Object-based superresolution land-cover mapping from remotely sensed imagery / Yuehong Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)
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Titre : Object-based superresolution land-cover mapping from remotely sensed imagery Type de document : Article/Communication Auteurs : Yuehong Chen, Auteur ; Yong Ge, Auteur ; Gerard B.M. Heuvelink, Auteur ; Ru An, Auteur ; Yu Chen, Auteur Année de publication : 2018 Article en page(s) : pp 328 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification orientée objet
[Termes IGN] classification pixellaire
[Termes IGN] déconvolution
[Termes IGN] krigeage
[Termes IGN] occupation du sol
[Termes IGN] programmation linéaire
[Termes IGN] variogrammeRésumé : (Auteur) Superresolution mapping (SRM) is a widely used technique to address the mixed pixel problem in pixel-based classification. Advanced object-based classification will face a similar mixed phenomenon-a mixed object that contains different land-cover classes. Currently, most SRM approaches focus on estimating the spatial location of classes within mixed pixels in pixel-based classification. Little if any consideration has been given to predicting where classes spatially distribute within mixed objects. This paper, therefore, proposes a new object-based SRM strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, an area-to-point kriging method is applied to predict the soft class values of subpixels within each object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object level is built to determine the optimal class labels of subpixels within each object. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM generated more land-cover details within mixed objects than did the traditional object-based hard classification and performed better than an existing pixel-based SRM method. Hence, OSRM provides a valuable solution to mixed objects in object-based classification. Numéro de notice : A2018-186 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2747624 Date de publication en ligne : 20/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2747624 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89843
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 328 - 340[article]Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing / Ali Akbari (2018)
Titre : Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing Type de document : Thèse/HDR Auteurs : Ali Akbari, Auteur Editeur : Paris : Sorbonne Université Année de publication : 2018 Importance : 158 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de Traitement du signal et de l’image, Sorbonne UniversitéLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] acquisition comprimée
[Termes IGN] compensation
[Termes IGN] compression d'image
[Termes IGN] modélisation
[Termes IGN] problème inverse
[Termes IGN] reconstruction d'image
[Termes IGN] représentation parcimonieuse
[Termes IGN] théorie du signalIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Signal models are a cornerstone of contemporary signal and image processing methodology. In this report, two particular signal modeling methods, called analysis and synthesis sparse representation, are studied which have been proven to be effective for many signals, such as natural images, and successfully used in a wide range of applications. Both models represent signals in terms of linear combinations of an underlying set, called dictionary, of elementary signals known as atoms. The driving force behind both models is sparsity of the representation coefficients, i.e. the rapid decay of the representation coefficients over the dictionary. On the other hands, the dictionary choice determines the success of the entire model. According to these two signal models, there have been two main disciplines of dictionary designing; harmonic analysis approach and machine learning methodology. The former leads to designing the dictionaries with easy and fast implementation, while the latter provides a simple and expressive structure for designing adaptable and efficient dictionaries. The main goal of this thesis is to provide new applications to these signal modeling methods by addressing several problems from various perspectives. It begins with the direct application of the sparse representation, i.e. image compression. The line of research followed in this area is the synthesis-based sparse representation approach in the sense that the dictionary is not fixed and predefined, but learned from training data and adapted to data, yielding a more compact representation. A new Image codec based on adaptive sparse representation over a trained dictionary is proposed, wherein different sparsity levels are assigned to the image patches belonging to the salient regions, being more conspicuous to the human visual system. Experimental results show that the proposed method outperforms the existing image coding standards, such as JPEG and JPEG2000, which use an analytic dictionary, as well as the state-of-the-art codecs based on the trained dictionaries. In the next part of thesis, it focuses on another important application of the sparse signal modeling, i.e. solving inverse problems, especially for error concealment (EC), wherein a corrupted image is reconstructed from the incomplete data, and Compressed Sensing recover, where an image is reconstructed from a limited number of random measurements. Signal modeling is usually used as a prior knowledge about the signal to solve these NP-hard problems. In this thesis, inspired by the analysis and synthesis sparse models, these challenges are transferred into two distinct sparse recovery frameworks and several recovery methods are proposed. Compared with the state-of-the-art EC and CS algorithms, experimental results show that the proposed methods show better reconstruction performance in terms of objective and subjective evaluations. This thesis is finalized by giving some conclusions and introducing some lines for future works. Note de contenu : 1- Introduction
2- Sparsity-based signal models
3- Image compressed sensing recovery
4- Receiver-based error concealment based on synthesis sparse recovery
5- Transmitter-based error concealment based on sparse recovery
6- Sparse representation-based image compression
7- Conclusion and future directionsNuméro de notice : 25937 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Traitement du signal et de l’image : Paris : 2018 nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2018SORUS461 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96286 Robust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)
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Titre : Robust minimum volume simplex analysis for hyperspectral unmixing Type de document : Article/Communication Auteurs : Shaoquan Zhang, Auteur ; Alexander Agathos, Auteur ; Jun Li, Auteur Année de publication : 2017 Article en page(s) : pp 6431 - 6439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du simplexe
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] factorisation
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robusteRésumé : (Auteur) Most blind hyperspectral unmixing methods exploit convex geometry properties of hyperspectral data. The minimum volume simplex analysis (MVSA) is one of such methods, which, as many others, estimates the minimum volume (MV) simplex where the measured vectors live. MVSA was conceived to circumvent the matrix factorization step often implemented by MV-based algorithms and also to cope with outliers, which compromise the results produced by MV algorithms. Inspired by the recently proposed robust MV enclosing simplex (RMVES) algorithm, we herein introduce the robust MVSA (RMVSA), which is a version of MVSA robust to noise. As in RMVES, the robustness is achieved by employing chance constraints, which control the volume of the resulting simplex. RMVSA differs, however, substantially from RMVES in the way optimization is carried out. In this paper, we develop a linearization relaxation of the nonlinear chance constraints, which can greatly lighten the computational complex of chance constraint problems. The effectiveness of RMVSA is illustrated by comparing its performance with the state of the art. Numéro de notice : A2017-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2728104 En ligne : https://doi.org/10.1109/TGRS.2017.2728104 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88784
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6431 - 6439[article]Linking ecosystem services with state-and-transition models to evaluate rangeland management decisions / Sapana Lohani in Global ecology and conservation, vol 8 (October 2016)
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Titre : Linking ecosystem services with state-and-transition models to evaluate rangeland management decisions Type de document : Article/Communication Auteurs : Sapana Lohani, Auteur ; Philip Heilman, Auteur ; J. Edward de Steiguer, Auteur ; D. Phillip Guertin, Auteur Année de publication : 2016 Article en page(s) : pp 58 - 70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Ecologie
[Termes IGN] Etats-Unis
[Termes IGN] modèle numérique
[Termes IGN] programmation linéaire
[Termes IGN] service écosystémiqueRésumé : (auteur) We simplified the existing state-and-transition models for rangelands using remote sensing technologies over larger landscapes. We developed an optimization model to include ecosystem services and disservices in the state-and-transition models. Linear programming optimization model was run for private and public ranchers. Net benefit was maximum for the public rancher when ecosystem disservices were excluded. Rangeland managers can use the optimization model to compare different management alternatives. Numéro de notice : A2016-714 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1016/j.gecco.2016.08.003 En ligne : http://dx.doi.org/10.1016/j.gecco.2016.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82107
in Global ecology and conservation > vol 8 (October 2016) . - pp 58 - 70[article]Recursive orthogonal projection-based simplex growing algorithm / Hsiao-Chi Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkGestion de collecte des déchets post-inondation / Oumayma Kaabi (2016)PermalinkPermalinkMinimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkMultibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing / Lei Liang in Journal of applied remote sensing, vol 9 (2015)PermalinkHigh-resolution fully polarimetric ISAR imaging based on compressive sensing / Wei Qiu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)PermalinkA comparative analysis of traveling salesman solutions from geographic information systems / Kevin M. Curtin in Transactions in GIS, vol 18 n° 2 (April 2014)PermalinkAttraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery / Xiaohua Tong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkSampling piecewise convex unmixing and endmember extraction / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)PermalinkJoint wall mitigation and compressive sensing for indoor image reconstruction / E. Lagunas in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)Permalink