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An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) / Kambiz Borna in Transactions in GIS, vol 20 n° 3 (June 2016)  

Public [article]  inTransactions in GIS > vol 20 n° 3 (June 2016) . - pp 368–381 Titre : An intelligent geospatial processing unit for image classification based on geographic vector agents (GVAs) Type de document : Article/Communication Auteurs : Kambiz Borna, Auteur ; Antoni B. Moore, Auteur ; Pascal Sirguey, Auteur Année de publication : 2016 Article en page(s) : pp 368–381 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agent (intelligence artificielle) [Termes IGN] analyse d'image orientée objet [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] données vectorielles [Termes IGN] image Ikonos [Termes IGN] modèle orienté agent Résumé : (auteur) Spatial modeling methods usually use pixels and image objects as fundamental processing units to address real-world objects, geo-objects, in image space. To do this, both pixel-based and object-based approaches typically employ a linear two-staged workflow of segmentation and classification. Pixel-based methods segment a classified image to address geo-objects in image space. In contrast, object-based approaches classify a segmented image to identify geo-objects from raster datasets. These methods lack the ability to simultaneously integrate the geometry and theme of geo-objects in image space. This article explores Geographical Vector Agents (GVAs) as an automated and intelligent processing unit to directly address real-world objects in the process of remote sensing image classification. The GVA is a distinct type of geographic automata characterized by elastic geometry, dynamic internal structure, neighborhoods and their respective rules. We test this concept by modeling a set of objects on a subset IKONOS image and LiDAR DSM datasets without the setting parameters (e.g. scale, shape information), usually applied in conventional Geographic Object-Based Image Analysis (GEOBIA) approaches. The results show that the GVA approach achieves more than 3.5% improvement for correctness, 2% improvement for quality, although no significant improvement for completeness to GEOBIA, thus demonstrating the competitive performance of GVAs classification. Numéro de notice : A2016-460 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12226 En ligne : http://dx.doi.org/10.1111/tgis.12226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81390 [article]