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Wetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar / Lian P. Rampi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 5 (May 2014)
[article]
Titre : Wetland mapping in the upper midwest United States: An object-based approach integrating Lidar and imagery radar Type de document : Article/Communication Auteurs : Lian P. Rampi, Auteur ; Joseph F. Knight, Auteur ; Keith C. Pelletier, Auteur Année de publication : 2014 Article en page(s) : pp 439 - 449 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] image radar
[Termes IGN] marais
[Termes IGN] Minnesota (Etats-Unis)Résumé : (Auteur) This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer's and user's accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods. Numéro de notice : A2014-243 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.5.439 En ligne : https://doi.org/10.14358/PERS.80.5.439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33146
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 5 (May 2014) . - pp 439 - 449[article]Bayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
[article]
Titre : Bayesian context-dependent learning for anomaly classification in hyperspectral imagery Type de document : Article/Communication Auteurs : Christopher Ratto, Auteur ; Kenneth D. Morton, Auteur ; Leslie M. Collins, Auteur ; Peter A. Torrione, Auteur Année de publication : 2014 Article en page(s) : pp 1969 - 1981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification contextuelle
[Termes IGN] détection d'objet
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] méthode robuste
[Termes IGN] rayonnement infrarougeRésumé : (Auteur) Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain. Numéro de notice : A2014-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2257175 En ligne : https://doi.org/10.1109/TGRS.2013.2257175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33170
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 4 (April 2014) . - pp 1969 - 1981[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014041 RAB Revue Centre de documentation En réserve L003 Disponible Automated parameterisation for multi-scale image segmentation on multiple layers / L. Drăguț in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Automated parameterisation for multi-scale image segmentation on multiple layers Type de document : Article/Communication Auteurs : L. Drăguț, Auteur ; O. Csillik, Auteur ; C. Eisank, Auteur ; D. Tiede, Auteur Année de publication : 2014 Article en page(s) : pp 119 - 127 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] eCognition
[Termes IGN] facteur d'échelle
[Termes IGN] résolution multiple
[Termes IGN] segmentation d'image
[Termes IGN] varianceRésumé : (Auteur) We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multi-resolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis. Numéro de notice : A2014-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.018 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32993
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 119 - 127[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Model-based analysis–synthesis for realistic tree reconstruction and growth simulation / Corina Iovan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
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Titre : Model-based analysis–synthesis for realistic tree reconstruction and growth simulation Type de document : Article/Communication Auteurs : Corina Iovan , Auteur ; Paul-Henri Cournède, Auteur ; Thomas Guyard, Auteur ; Benoit Bayol, Auteur ; Didier Boldo , Auteur ; Matthieu Cord, Auteur Année de publication : 2014 Article en page(s) : pp 1438 - 1450 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre (flore)
[Termes IGN] arbre urbain
[Termes IGN] croissance des arbres
[Termes IGN] détection d'arbres
[Termes IGN] dynamique de la végétation
[Termes IGN] extraction de la végétation
[Termes IGN] image aérienne
[Termes IGN] modèle de croissance végétale
[Termes IGN] reconstruction d'objetRésumé : (auteur) Due to complexity, vegetation analysis and reconstruction of remote sensing data are challenging problems. Using architectural tree models combined with model inputs estimated from aerial image analysis, this paper presents an analysis-synthesis approach for urban vegetation detection, modeling, and reconstruction. Tree species, height, and crown size information are extracted by aerial image analysis. These variables serve for model inversion to retrieve plant age, climatic growth conditions, and competition with neighbors. Functional-structural individual-based tree models are used to reconstruct and visualize virtual trees and their time evolutions realistically in a 3-D viewer rendering the models with geographical coordinates in the reconstructed scene. Our main contributions are: 1) a novel approach for generating plant models in 3-D reconstructed scenes based on the analysis of the geometric properties of the data, and 2) a modeling workflow for the reconstruction and growth simulation of vegetation in urban or natural environments. Numéro de notice : A2014-815 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2251467 Date de publication en ligne : 12/04/2013 En ligne : https://doi.org/10.1109/TGRS.2013.2251467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92035
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1438 - 1450[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Multi-agent recognition system based on object based image analysis using WorldView-2 / Fatemeh Tabib Mahmoudi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)
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Titre : Multi-agent recognition system based on object based image analysis using WorldView-2 Type de document : Article/Communication Auteurs : Fatemeh Tabib Mahmoudi, Auteur ; Farhad Samadzadegan, Auteur ; Peter Reinartz, Auteur Année de publication : 2014 Article en page(s) : pp 161 - 170 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification à base de connaissances
[Termes IGN] détection de régions
[Termes IGN] image Worldview
[Termes IGN] reconnaissance d'objets
[Termes IGN] système multi-agents
[Termes IGN] zone urbaine denseRésumé : (Auteur) In this paper, using spatial and spectral characteristics of the WorldView-2 satellite imagery, capabilities of multi-agent systems are used for solving multiple object recognition difficulties in complex urban areas. The methodology has two main steps: object based image analysis (OBIA) and multi-agent object recognition. In the first step, segmentation and multi-process object classification based on spectral, textura, and structural features are performed. Classified regions are used as an input dataset in the multi-agent system in order to modify object recognition results. According to the results from the object based image analysis process, using contextual relations and structural features, the overall accuracy and Kappa improved by 17.79 percent and 0.253, respectively. Using knowledge-based reasoning and cooperative capabilities of agents in the multi-agent system in this paper most of the remaining difficulties are decreased and values 90.95 percent and 0.876 are obtained for the overall accuracy and Kappa, respectively, of the object recognition results. Numéro de notice : A2014-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.161-170 En ligne : https://doi.org/10.14358/PERS.80.2.161-170 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33014
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 161 - 170[article]Assessment of the image misregistration effects on object-based change detection / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkComparaison de méthodes d'extraction automatique à partir d'images multispectrales / Valerio Baiocchi in Géomatique expert, n° 96 (01/01/2014)PermalinkPermalinkReconstruction de modèles 3D photoréalistes de façades à partir de données image et laser terrestre / Jérôme Demantké (2014)PermalinkParcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkIntelligent services for discovery of complex geospatial features from remote sensing imagery / Peng Yue in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)PermalinkProgress in marine oil spill optical remote sensing: Detected targets, spectral response characteristics, and theories / Lu yingcheng in Marine geodesy, vol 36 n° 3 (September - November 2013)PermalinkAdvances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective / Damien Arvor in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)PermalinkCartographie et suivi de la densité des arbres de l'arganeraie (Sud-Ouest du Maroc) à partir d'images de télédétection à haute résolution spatiale / Mbark Aouragh in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)PermalinkComparaison entre les méthodes J-SEG et MeanShift : application sur des données THRS / Rabia Sarah Cheriguene in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)Permalink