Descripteur
Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > analyse d'image orientée objet
analyse d'image orientée objetVoir aussi |
Documents disponibles dans cette catégorie (482)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
Etendre la recherche sur niveau(x) vers le bas
A fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
![]()
[article]
Titre : A fuzzy spatial reasoner for multi-scale GEOBIA ontologies Type de document : Article/Communication Auteurs : Argyros Argyridis, Auteur ; Demetre P. Argialas, Auteur Année de publication : 2015 Article en page(s) : pp 491 - 498 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 du bâti
[Termes IGN] image Quickbird
[Termes IGN] objet géographique
[Termes IGN] ontologie
[Termes IGN] OWL
[Termes IGN] PostgreSQL
[Termes IGN] segmentation d'image
[Termes IGN] toitRésumé : (auteur) In Geographic Object-Based Image Analysis (GEOBIA), an image is partitioned into objects by a segmentation algorithm. These objects are then classified into semantic categories based on unsupervised/ supervised methods, or knowledge-based methods, such as an ontology. The aim of this paper was to develop a SPatial Ontology Reasoner (SPOR) to allow the development of GEOBIA ontologies by employing fuzzy, spatial, and multi-scale representations, with time efficiency. An enhanced version of the Web Ontology Language 2 (OWL 2) with fuzzy representations was adopted and expanded to represent fuzzy spatial relationships within the framework of GEOBIA. Segmentation results are stored within PostgreSQL. An ontology described the class/subclass hierarchy and class definitions. SPOR integrated PostgreSQL and the ontology, to classify the objects. To demonstrate the framework, a QuickBird image was employed for building extraction. Accuracy assessment indicated that 87 percent of building rooftops were detected. Numéro de notice : A2015-979 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.491 En ligne : https://doi.org/10.14358/PERS.81.6.491 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80062
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 491 - 498[article]Mangrove tree crown delineation from high-resolution imagery / Muditha K. Heenkenda in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
![]()
[article]
Titre : Mangrove tree crown delineation from high-resolution imagery Type de document : Article/Communication Auteurs : Muditha K. Heenkenda, Auteur ; Karen E. Joyce, Auteur ; Stefan W. Maier, Auteur Année de publication : 2015 Article en page(s) : pp 471 - 479 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] croissance des arbres
[Termes IGN] houppier
[Termes IGN] image à haute résolution
[Termes IGN] image Worldview
[Termes IGN] mangrove
[Termes IGN] modèle numérique de surface
[Termes IGN] objet géographiqueRésumé : (auteur) Mangroves are very dense, spatially heterogeneous, and have limited height variations between neighboring trees. Delineating individual tree crowns is thus very challenging. This study compared methods for isolating mangrove crowns using object based image analysis. A combination of WorldView-2 imagery, a digital surface model, a local maximum filtering technique, and a region growing approach achieved 92 percent overall accuracy in extracting tree crowns. The more traditionally used inverse watershed segmentation method showed low accuracy (35 percent), demonstrating that this method is better suited to homogeneous forests with reasonable height variations between trees. The main challenges with each of the methods tested were the limited height variation between surrounding trees and multiple upward pointing branches of trees. In summary, mangrove tree crowns can be delineated from appropriately parameterized objectbased algorithms with a combination of high-resolution satellite images and a digital surface model. We recommend partitioning the imagery into homogeneous species stands for best results. Numéro de notice : A2015-977 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.471 En ligne : https://doi.org/10.14358/PERS.81.6.471 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80060
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 471 - 479[article]Object-based building change detection from a single multispectral image and pre-existing geospatial information / Georgia Doxani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
![]()
[article]
Titre : Object-based building change detection from a single multispectral image and pre-existing geospatial information Type de document : Article/Communication Auteurs : Georgia Doxani, Auteur ; Konstantinos Karantzalos, Auteur ; Maria Tsakiri-Strati, Auteur Année de publication : 2015 Article en page(s) : pp 481 - 489 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse de l'existant
[Termes IGN] analyse diachronique
[Termes IGN] base de données localisées
[Termes IGN] bâtiment
[Termes IGN] classification à base de connaissances
[Termes IGN] détection de changement
[Termes IGN] Grèce
[Termes IGN] image isolée
[Termes IGN] image multibande
[Termes IGN] milieu urbainRésumé : (auteur) Multispectral images of very high spatial resolution and vector data from geospatial databases, such as cadastral maps, are among the cost-effective and broadly available geodata in urban environments. Therefore, we aim to address building change detection based on pre-existing building footprint information and a single very high resolution multispectral image. An object-based classification methodology was developed that employs advanced scalespace filtering, unsupervised clustering, and knowledge-based classification. The developed framework effectively integrates prior vector data and multispectral observations, through incorporating the prior knowledge into the training process and defining the proper object-based classification rules. The methodology successfully identified important building changes, which were validated by employing the vector information of a building geodatabase and a QuickBird image acquired in 2003 and 2007, respectively, over urban regions in the city of Thessaloniki, Greece. The performed quantitative and qualitative evaluation indicated that the proposed analysis framework can detect the new buildings with high accuracy rates and, to a lesser degree, their exact shape and size. Numéro de notice : A2015-978 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.481 En ligne : https://doi.org/10.14358/PERS.81.6.481 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80061
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 481 - 489[article]Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning / Junwei Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
![]()
[article]
Titre : Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning Type de document : Article/Communication Auteurs : Junwei Han, Auteur ; Dingwen Zhang, Auteur ; Gong Cheng, Auteur Année de publication : 2015 Article en page(s) : pp 3325 - 3337 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] détection d'objet
[Termes IGN] estimation bayesienne
[Termes IGN] état de l'art
[Termes IGN] moteur d'inférenceRésumé : (Auteur) The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches. Numéro de notice : A2015 - 283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2374218 Date de publication en ligne : 18/12/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2374218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76400
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3325 - 3337[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
![]()
[article]
Titre : A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification Type de document : Article/Communication Auteurs : Z. Wang, Auteur ; Liqiang Zhang, Auteur ; Tian Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2409 - 2425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification orientée objet
[Termes IGN] détection de piéton
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] lasergrammétrie
[Termes IGN] objet mobile
[Termes IGN] semis de points
[Termes IGN] structure hiérarchique de données
[Termes IGN] télémétrie laser terrestre
[Termes IGN] zone urbaine denseRésumé : (Auteur) The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars. Numéro de notice : A2015-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2359951 En ligne : https://doi.org/10.1109/TGRS.2014.2359951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77533
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2409 - 2425[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Refining high spatial resolution remote sensing image segmentation for man-made objects through acollinear and ipsilateral neighborhood model / Min Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)
PermalinkClassifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkObject-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkTraining set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkContextual classification of point cloud data by exploiting individual 3d neigbourhoods / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
![]()
PermalinkExtracting mobile objects in images using a Velodyne lidar point cloud / Bruno Vallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
PermalinkCoregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information / Janja Avbelj in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)
PermalinkPermalinkEtude de l'évolution de l'utilisation du sol dans le district Sunsari (plaine du Népal) depuis les années 1950 / Mathilde Dumont-Aublin (2015)
PermalinkEtude expérimentale en cartographie de la végétation par télédétection / Vanessa Sellin in Cybergeo, European journal of geography, n° 2015 ([01/01/2015])
Permalink