European journal of remote sensing . vol 51 n° 1Paru le : 01/03/2018 |
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Ajouter le résultat dans votre panierContextual classification using photometry and elevation data for damage detection after an earthquake event / Ewelina Rupnik in European journal of remote sensing, vol 51 n° 1 (2018)
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Titre : Contextual classification using photometry and elevation data for damage detection after an earthquake event Type de document : Article/Communication Auteurs : Ewelina Rupnik , Auteur ; Francesco Nex, Auteur ; Isabella Toschi, Auteur ; Fabio Remondino, Auteur Année de publication : 2018 Projets : 3-projet - voir note / Article en page(s) : pp 543 - 557 Note générale : bibliographie
This work was supported by RAPIDMAP, a CONCERT-Japan project, i.e. a European Union (EU) funded project in the International Cooperation Activities under the Capacities Programme the 7th Framework Programme for Research and Technology Development. https://cordis.europa.eu/project/id/266604/reportingLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cartographie d'urgence
[Termes IGN] chaîne de traitement
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification contextuelle
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] détection de changement
[Termes IGN] dommage matériel
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] photométrie
[Termes IGN] prise en compte du contexte
[Termes IGN] zone urbaineRésumé : (auteur) This research presents a processing workflow to automatically find damaged building areas in an urban context. The input data requirements are high-resolution multi-view images, acquired from airborne platform. The elevations are derived from a dense surface model generated with photogrammetric methods. With the principal objective of rapid response in emergency situations, two different processing roadmaps are proposed, semi-supervised and unsupervised. Both of them follow a two-step workflow of building detection and building health estimation. Optionally, cadastral layers may serve as a-priori knowledge on building location. The semi-supervised approach involves a data training step, while the unsupervised approach exploits the similarities and dissimilarities between sets of features calculated over the detected buildings. The change detection task is formulated as a classification task defined over a conditional random field. The algorithms are evaluated using two datasets (Vexcel and Midas cameras) and results are compared with ground truth data and specific metrics. Numéro de notice : A2018-664 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2018.1458584 Date de publication en ligne : 16/05/2018 En ligne : https://doi.org/10.1080/22797254.2018.1458584 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94250
in European journal of remote sensing > vol 51 n° 1 (2018) . - pp 543 - 557[article]