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Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA / Ming Dongping in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 11 (November 2018) . - pp 681-693 Titre : Coupling relationship among scale parameter, segmentation accuracy, and classification accuracy in GeOBIA Type de document : Article/Communication Auteurs : Ming Dongping, Auteur ; Wen Zhou, Auteur ; Xu Lu, Auteur ; Min Wang, Auteur ; Yanni Ma, Auteur Année de publication : 2018 Article en page(s) : pp 681-693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] précision de la classification [Termes IGN] segmentation d'image Résumé : (Auteur) The quality of multi-scale segmentation mainly consists of intrasegment homogeneity and intersegment heterogeneity; however, it is difficult to synchronously get both high. It is crucial to make it clear which one of these two measures is more important and what is the coupling relationship among segmentation scale parameter, image segmentation and classification accuracy. This paper employs series of segmentation and classification to show that (1) intrasegment homogeneity is more important than intersegment heterogeneity in GeOBIA; there is always highly positive correlation between intrasegment homogeneity and classification accuracy; (2) with the increase of spectral heterogeneity parameter, both image object amount and the intrasegment homogeneity decrease; however the intersegment heterogeneity increases or increases first then decrease after the appropriate scale; and (3) the appropriate scale means there is a compromise between intrasegment homogeneity and intersegment heterogeneity. The research findings are helpful to raise awareness among practitioners who suffer from scale issues in GeOBIA. Numéro de notice : A2018-484 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.11.681 Date de publication en ligne : 01/11/2018 En ligne : https://doi.org/10.14358/PERS.84.11.681 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91209 [article]

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Triangulating social multimedia content for event localization using Flickr and Twitter / George Panteras in Transactions in GIS, vol 19 n° 5 (October 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 5 (October 2015) . - pp 694 – 715 Titre : Triangulating social multimedia content for event localization using Flickr and Twitter Type de document : Article/Communication Auteurs : George Panteras, Auteur ; Sarah Wise, Auteur ; Xu Lu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 694 – 715 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse d'image numérique [Termes IGN] catastrophe naturelle [Termes IGN] détection de contours [Termes IGN] géopositionnement [Termes IGN] image Flickr [Termes IGN] orientation d'image [Termes IGN] réseau social [Termes IGN] Twitter [Termes IGN] zone d'intérêt Résumé : (auteur) The analysis of social media content for the extraction of geospatial information and event-related knowledge has recently received substantial attention. In this article we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses both Twitter and Flickr content in an integrated two-step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient Flickr imagery accordingly and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The article presents our approach and demonstrates its performance using a real-world wildfire event as a representative application case study. Numéro de notice : A2015-683 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12122 En ligne : http://dx.doi.org/10.1111/tgis.12122 Format de la ressource électronique : Url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78316 [article]