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Modelling forest canopy trends with on-demand spatial simulation / Gordon M. Green in International journal of geographical information science IJGIS, vol 30 n° 1-2 (January - February 2016)
[article]
Titre : Modelling forest canopy trends with on-demand spatial simulation Type de document : Article/Communication Auteurs : Gordon M. Green, Auteur ; Sean C. Ahearn, Auteur Année de publication : 2016 Article en page(s) : pp 61 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] champ aléatoire de Markov
[Termes IGN] forêt
[Termes IGN] image Terra-MODIS
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] service web géographique
[Termes IGN] simulation numériqueRésumé : (auteur) Understanding trends in forest canopy cover at local, national, and global scales is important for many applications, including policymaking related to forest carbon sequestration. Globally consistent land-cover data sets derived from MODerate-resolution Imaging Spectroradiometer (MODIS) are now available for a period of more than 10 years, long enough to detect trends both in deforestation and in afforestation. However, methods of modelling land-cover change normally require specialized software and expertise, limiting the availability of this information. This barrier to access can be eliminated through the use of web services that construct models on demand based on user-specified regions of interest, so that parameters are inferred from, and relevant to, local conditions. In this paper we present a proof-of-concept system for building and running spatial Markov chain models of forest-cover change on demand, and demonstrate how the on-demand approach may be implemented for similar applications. Numéro de notice : A2016-010 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1066791 En ligne : http://dx.doi.org/10.1080/13658816.2015.1066791 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79331
in International journal of geographical information science IJGIS > vol 30 n° 1-2 (January - February 2016) . - pp 61 - 73[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2016011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Probabilistic multi-person localisation and tracking Type de document : Thèse/HDR Auteurs : Tobias Klinger, Auteur ; Ingo Neumann, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2016 Collection : DGK - C, ISSN 0065-5325 num. 787 Importance : 125 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-7696-5199-7 Note générale : bibliographie
PhD DissertationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de sensibilité
[Termes IGN] analyse multicritère
[Termes IGN] classification
[Termes IGN] détection de piéton
[Termes IGN] géolocalisation
[Termes IGN] image isolée
[Termes IGN] modèle stochastique
[Termes IGN] objet mobile
[Termes IGN] piéton
[Termes IGN] poursuite de cible
[Termes IGN] programmation linéaire
[Termes IGN] séquence d'images
[Termes IGN] similitude
[Termes IGN] surveillanceRésumé : (auteur) This dissertation investigates the problem of localising multiple persons in image sequences, while, at the same time, establishing temporal correspondences between single-frame locations. The aim of this work is the improvement of the reliability and precision of the generated trajectories, which is addressed by the formulation and investigation of a joint probabilistic model for the recursive filtering of the estimated positions. The trajectories are estimated in a common 3D object coordinate system, which was previously almost exclusively done in 2D. Note de contenu : 1. Introduction
1.1. Motivation
1.2. Research objectives and contributions
1.3. Outline of the dissertation
2. Basics
2.1. Probabilistic modelling
2.2. Recursive Bayesian estimation
2.3. Gaussian Process Regression
3. Related work
3.1. Tracking approaches
3.2. Observations
3.3. Temporal modelling
3.4. Data association
3.5. Discussion
4. A new probabilistic approach for multi-person localisation and tracking
4.1. Problem statement via Dynamic Bayesian Network
4.2. Observations
4.3. Temporal model
4.4. data association
4.5. Recursive estimation
4.6. Discussion
5. Experiments
5.1. Datasets and evaluation criteria
5.2. Sensitivity study and training
5.3. Model validation by ablation of its components
5.4. Multi-person localisation and tracking evaluation
6. Discussion of the results
6.1. Method evaluation
6.2. Evaluation of the trajectories
7. Conclusions and future workNuméro de notice : 19793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Dissertation : : Stuttgart : 2016 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85037 Documents numériques
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Probabilistic multi-person localisation and trackingAdobe Acrobat PDF Stochastic geometrical model and Monte Carlo optimization methods for building reconstruction from InSAR data / Yue Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)
[article]
Titre : Stochastic geometrical model and Monte Carlo optimization methods for building reconstruction from InSAR data Type de document : Article/Communication Auteurs : Yue Zhang, Auteur ; Xuan Sun, Auteur ; Antje Thiele, Auteur ; Stefan Hinz, Auteur Année de publication : 2015 Article en page(s) : pp 49 – 61 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] image radar moirée
[Termes IGN] image TanDEM-X
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] reconstruction 3D du bâtiRésumé : (auteur) Synthetic aperture radar (SAR) systems, such as TanDEM-X, TerraSAR-X and Cosmo-SkyMed, acquire imagery with high spatial resolution (HR), making it possible to observe objects in urban areas with high detail. In this paper, we propose a new top-down framework for three-dimensional (3D) building reconstruction from HR interferometric SAR (InSAR) data. Unlike most methods proposed before, we adopt a generative model and utilize the reconstruction process by maximizing a posteriori estimation (MAP) through Monte Carlo methods. The reason for this strategy refers to the fact that the noisiness of SAR images calls for a thorough prior model to better cope with the inherent amplitude and phase fluctuations.
In the reconstruction process, according to the radar configuration and the building geometry, a 3D building hypothesis is mapped to the SAR image plane and decomposed to feature regions such as layover, corner line, and shadow. Then, the statistical properties of intensity, interferometric phase and coherence of each region are explored respectively, and are included as region terms. Roofs are not directly considered as they are mixed with wall into layover area in most cases. When estimating the similarity between the building hypothesis and the real data, the prior, the region term, together with the edge term related to the contours of layover and corner line, are taken into consideration. In the optimization step, in order to achieve convergent reconstruction outputs and get rid of local extrema, special transition kernels are designed. The proposed framework is evaluated on the TanDEM-X dataset and performs well for buildings reconstruction.Numéro de notice : A2015-851 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.06.004 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.06.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79221
in ISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 49 – 61[article]Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field / Miaozhong Xu in Geocarto international, vol 30 n° 9 - 10 (October - November 2015)
[article]
Titre : Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field Type de document : Article/Communication Auteurs : Miaozhong Xu, Auteur ; Ming Cong, Auteur ; Tianpeng Xie, Auteur Année de publication : 2015 Article en page(s) : pp 997 - 1015 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] filtre de Gabor
[Termes IGN] segmentation d'image
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Here, we describe an unsupervised segmentation method incorporating log-Gabor (LG) filters and a Markov random field (MRF) model for high-resolution (HR) remote sensing (RS) images, based on classical models of the visual receptive field. LG filters were utilised to model the receptive fields of the simple cells in the primary visual cortex and extract detailed features from HR–RS images followed by construction of image pyramid through wavelet decomposition to simulate the hierarchical structure of the visual sensing system. Finally, based on the original HR–RS images, their detailed features and the image pyramid, the MRF image segmentation model was applied to obtain the final segmentation result. Real HR–RS images were used as experimental data to validate the proposed method, both qualitatively (visually) and numerically (with the overall accuracy and Kappa index).The experimental results indicate that the proposed method is effective, feasible and robust to noise. Numéro de notice : A2015-627 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1006529 Date de publication en ligne : 26/02/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1006529 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78110
in Geocarto international > vol 30 n° 9 - 10 (October - November 2015) . - pp 997 - 1015[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2015051 RAB Revue Centre de documentation En réserve L003 Disponible A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS / Abubrakr A. A. Al Sharif in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)
[article]
Titre : A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS Type de document : Article/Communication Auteurs : Abubrakr A. A. Al Sharif, Auteur ; Biswajeet Pradhan, Auteur Année de publication : 2015 Article en page(s) : pp 858 - 881 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] arbre de décision
[Termes IGN] automate cellulaire
[Termes IGN] croissance urbaine
[Termes IGN] données spatiotemporelles
[Termes IGN] étalement urbain
[Termes IGN] khi carré
[Termes IGN] modèle de simulation
[Termes IGN] modèle dynamique
[Termes IGN] modèle stochastique
[Termes IGN] surveillance de l'urbanisation
[Termes IGN] système d'information géographique
[Termes IGN] Tripoli (Libye ; ville)
[Termes IGN] urbanisationRésumé : (Auteur) Urban development is a continuous and dynamic spatio-temporal phenomenon associated with economic developments and growing populations. To understand urban expansion, it is important to establish models that can simulate urbanization process and its deriving factors behaviours, monitor deriving forces interactions and predict spatio-temporally probable future urban growth patterns explicitly. In this research, therefore, we presented a hybrid model that integrates the chi-squared automatic integration detection decision tree (CHAID-DT), Markov chain (MC) and cellular automata (CA) models to analyse, simulate and predict future urban expansions in Tripoli, Libya in 2020 and 2025. First, CHAID-DT model was applied to investigate the contributions of urban factors to the expansion process, to explore their interactions and to provide future urban probability map; second, MC model was employed to estimate the future demand of urban land; third, CA model was used to allocate estimated urban land quantity on the probability map to present future projected land use map. Three satellite images of the study area were obtained from the periods of 1984, 2002 and 2010 to extract land use maps and urban expansion data. We validated the model with two methods, namely, receiver operating characteristic and the kappa statistic index of agreement. Results confirmed that the proposed hybrid model could be employed in urban expansion modelling. The applied hybrid model overcame the individual shortcomings of each model and explicitly described urban expansion dynamics, as well as the spatio-temporal patterns involved. Numéro de notice : A2015-504 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.997308 Date de publication en ligne : 10/02/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2014.997308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77421
in Geocarto international > vol 30 n° 7 - 8 (August - September 2015) . - pp 858 - 881[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2015041 RAB Revue Centre de documentation En réserve L003 Disponible A probabilistic eco-hydrological model to predict the effects of climate change on natural vegetation at a regional scale / Jan-Philip M. Witte in Landscape ecology, vol 30 n° 5 (May 2015)PermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkRegional gold potential mapping in Kelantan (Malaysia) using probabilistic based models and GIS / Suhaimizi Yusoff in Open geosciences, vol 7 n° 1 (January 2015)PermalinkEvaluation of feature-based 3-d registration of probabilistic volumetric scenes / Maria I. Restrepo in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)PermalinkA hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario, Canada / Junjie Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)PermalinkSingle frequency GPS/Galileo precise point positioning using un-differenced and between-satellite single difference measurements / Akram Afifi in Geomatica, vol 68 n° 3 (September 2014)PermalinkAn intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata based algorithm / P. V. Arun in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)PermalinkA class of cloud detection algorithms based on a MAP-MRF approach in space and time / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkCloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model / Qing Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 92 (June 2014)Permalink