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Estimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)
[article]
Titre : Estimation of forest biomass using multivariate relevance vector regression Type de document : Article/Communication Auteurs : Alireza Sharifi, Auteur ; Jalal Amini, Auteur ; Ryutaro Tateishi, Auteur Année de publication : 2016 Article en page(s) : pp 41 - 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse aérienne
[Termes IGN] biomasse forestière
[Termes IGN] estimation statistique
[Termes IGN] forêt
[Termes IGN] image ALOS-PALSAR
[Termes IGN] Iran
[Termes IGN] Perceptron multicouche
[Termes IGN] régression multiple
[Termes IGN] séparateur à vaste marge
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The objective of this study is to develop a method based on multivariate relevance vector regression (MVRVR) as a kernelbased Bayesian model for the estimation of above-ground biomass (AGB) in the Hyrcanian forests of Iran. Field AGB data from the Hyrcanian forests and multi-temporal PALSAR backscatter values are used for training and testing the methods. The results of the MVRVR method are then compared with other methods: multivariate linear regression (MLR), multilayer perceptron neural network (MLPNN), and support vector regression (SVR). The MLR model showed lower values of R2 than the three other approaches. Although the SVR model was found to be more accurate than MLPNN, it had the lowest saturation point of 224.75 Mg/ha. The use of MVRVR model significantly improves the estimation of AGB (R2 = 0.90; RMSE = 32.05 Mg/ha), and the model showed a superior performance in estimating AGB with the highest saturation point (297.81 Mg/ha). Numéro de notice : A2016-053 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14358/PERS.83.1.41 En ligne : https://doi.org/10.14358/PERS.83.1.41 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79654
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 1 (January 2016) . - pp 41 - 49[article]A back-propagation neural network-based approach for multi-represented feature matching in update propagation / Yanxia Wang in Transactions in GIS, vol 19 n° 6 (December 2015)
[article]
Titre : A back-propagation neural network-based approach for multi-represented feature matching in update propagation Type de document : Article/Communication Auteurs : Yanxia Wang, Auteur ; Deng Chen, Auteur ; Zhiyuan Zhao, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 964 – 993 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] mise à jour de base de données
[Termes IGN] objet géographique zonal
[Termes IGN] pondération
[Termes IGN] représentation multiple
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Spatial data infrastructures, which are characterized by multi-represented datasets, are prevalent throughout the world. The multi-represented datasets contain different representations for identical real-world entities. Therefore, update propagation is useful and required for maintaining multi-represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real-world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi-represented feature matching based on spatial similarity and a back-propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one-to-one matching but also to one-to-many and many-to-many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN-based feature matching approach. Numéro de notice : A2015--077 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12138 En ligne : http://dx.doi.org/10.1111/tgis.12138 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81352
in Transactions in GIS > vol 19 n° 6 (December 2015) . - pp 964 – 993[article]A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture / S. Basu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : A semiautomated probabilistic framework for tree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture Type de document : Article/Communication Auteurs : S. Basu, Auteur ; Sangram Ganguly, Auteur ; Ramakrishna R. Nemani, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5690 - 5708 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] architecture des systèmes d'information
[Termes IGN] classification non dirigée
[Termes IGN] couvert forestier
[Termes IGN] Etats-Unis
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation d'imageRésumé : (Auteur) Accurate tree-cover estimates are useful in deriving above-ground biomass density estimates from very high resolution (VHR) satellite imagery data. Numerous algorithms have been designed to perform tree-cover delineation in high-to-coarse-resolution satellite imagery, but most of them do not scale to terabytes of data, typical in these VHR data sets. In this paper, we present an automated probabilistic framework for the segmentation and classification of 1-m VHR data as obtained from the National Agriculture Imagery Program (NAIP) for deriving tree-cover estimates for the whole of Continental United States, using a high-performance computing architecture. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on conditional random field, which helps in capturing the higher order contextual dependence relations between neighboring pixels. Once the final probability maps are generated, the framework is updated and retrained by incorporating expert knowledge through the relabeling of misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates (FPRs). The tree-cover maps were generated for the state of California, which covers a total of 11 095 NAIP tiles and spans a total geographical area of 163 696 sq. miles. Our framework produced correct detection rates of around 88% for fragmented forests and 74% for urban tree-cover areas, with FPRs lower than 2% for both regions. Comparative studies with the National Land-Cover Data algorithm and the LiDAR high-resolution canopy height model showed the effectiveness of our algorithm for generating accurate high-resolution tree-cover maps. Numéro de notice : A2015-753 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2428197 Date de publication en ligne : 26/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2428197 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78743
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5690 - 5708[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible Prediction of traffic counts using statistical and neural network models / Abul Kalam Azad in Geomatica, vol 69 n° 3 (september 2015)
[article]
Titre : Prediction of traffic counts using statistical and neural network models Type de document : Article/Communication Auteurs : Abul Kalam Azad, Auteur Année de publication : 2015 Article en page(s) : pp 297 - 311 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse comparative
[Termes IGN] apprentissage dirigé
[Termes IGN] Calgary
[Termes IGN] intelligence artificielle
[Termes IGN] modèle statistique
[Termes IGN] prédiction
[Termes IGN] réseau neuronal artificiel
[Termes IGN] trafic urbain
[Termes IGN] utilisation du solRésumé : (auteur) Cet article compare deux modèles différents de prédiction du trafic basés sur des variables démographiques et d'utilisation des terres de la ville de Calgary. Les caractéristiques démographiques et d'utilisation des terres ont été utilisées comme variables indépendantes au niveau des aires de diffusion (AD — de petites unités géo graphiques avec une population variant entre 400 et 700 habitants) dans la ville de Calgary. On a utilisé les données de trafic routier de la ville de Calgary comme variable dépendante pour développer des modèles statistiques et des modèles de réseaux neuronaux. Des modèles statistiques de comptes binomiaux négatifs (avec carnet de bord en ligne) ont été élaborés puisque les données semblaient sur-dispersées. Les modèles de réseaux neuronaux ont été élaborés selon un concept de rétro-propagation multicouches en aval pour l'apprentissage supervisé. Les résultats indiquent que le modèle en réseau neuronal garantit un moins grand nombre d'erreurs que le modèle statistique. Dans l'ensemble, le modèle avec réseaux neuronaux a produit de meilleurs résultats pour la prédiction du trafic que l'approche de régression binominale négative également à l’étude dans cet article. Le modèle avec réseaux neuronaux convient particulièrement en raison de sa meilleure capacité de prédiction. Cependant, le modèle statistique peut être utilisé pour sa formulation mathématique ou pour élaborer une meilleure compréhension du rôle des variables explicatives dans l'estimation du trafic. Numéro de notice : A2015-667 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/cig2015-304 En ligne : https://doi.org/10.5623/cig2015-304 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78276
in Geomatica > vol 69 n° 3 (september 2015) . - pp 297 - 311[article]Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study / Hossein Shafizadeh-Moghadam in International journal of geographical information science IJGIS, vol 29 n° 4 (April 2015)
[article]
Titre : Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study Type de document : Article/Communication Auteurs : Hossein Shafizadeh-Moghadam, Auteur ; Julian Hagenauer, Auteur ; Manuchehr Farajzadeh, Auteur ; Marco Helbich, Auteur Année de publication : 2015 Article en page(s) : pp 606 - 623 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] Bombay
[Termes IGN] croissance urbaine
[Termes IGN] fonction de base radiale
[Termes IGN] milieu urbain
[Termes IGN] modèle de simulation
[Termes IGN] Perceptron multicouche
[Termes IGN] performance
[Termes IGN] test de performance
[Termes IGN] urbanisationRésumé : (Auteur) The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling. Numéro de notice : A2015-589 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.993989 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.993989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77875
in International journal of geographical information science IJGIS > vol 29 n° 4 (April 2015) . - pp 606 - 623[article]PermalinkLand cover and soil type mapping from spaceborne PolSAR Data at L-Band with probabilistic neural network / Oleg Antropov in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)PermalinkModel generalization of two different drainage patterns by self-organizing maps / Alper Sen in Cartography and Geographic Information Science, vol 41 n° 2 (March 2014)PermalinkUse of artificial neural networks for selective omission in updating road networks / Qi Zhou in Cartographic journal (the), vol 51 n° 1 (February 2014)PermalinkAssessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkPermalinkComparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points / Y. Shao in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)PermalinkAn assessment of internal neural network parameters affecting image classification accuracy / L. Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 12 (December 2011)PermalinkDevelopment of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)PermalinkSimilarity weighted instance-based learning for the generation of transition potentials in land use change modeling / F. Sangermano in Transactions in GIS, vol 14 n° 5 (October 2010)PermalinkA neural network-based method for solving "nested hierarchy" areal interpolation problems / D. Merwin in Cartography and Geographic Information Science, vol 36 n° 4 (October 2009)PermalinkPotentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes / Konstantinos Topouzelis in Geocarto international, vol 24 n° 3 (June - July 2009)PermalinkRepresenting geographical objects with scale-induced indeterminate boundaries: a neural network-based data model / José L. Silvan-Cardenas in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)Permalinkvol 74 n° 10 - October 2008 - Artificial intelligence in remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensingPermalinkUsing neural networks and cellular automata for modelling intra-urban land-use dynamics / C.M. 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