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A supervised classification approach towards quality self-diagnosis of 3D building models using digital aerial imagery / Laurence Boudet (2006)
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Titre : A supervised classification approach towards quality self-diagnosis of 3D building models using digital aerial imagery Type de document : Article/Communication Auteurs : Laurence Boudet , Auteur ; Nicolas Paparoditis
, Auteur ; Franck Jung
, Auteur ; Gilles Martinoty
, Auteur ; Marc Pierrot-Deseilligny
, Auteur
Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2006 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 36-3 Conférence : PCV 2006, ISPRS - Commission 3 symposium Photogrammetric Computer vision 20/09/2006 22/09/2006 Bonn Allemagne OA ISPRS Archives Importance : pp 136 - 141 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bati
[Termes IGN] classification dirigée
[Termes IGN] image aérienne
[Termes IGN] méthode robuste
[Termes IGN] modélisation 3D
[Termes IGN] toit
[Termes IGN] zone urbaine denseRésumé : (auteur) In the context of 3D building model production or updating, the models have to be manually checked one by one by a human operator in order to ensure their quality. In this paper, we investigate a new approach to perform a quality self-diagnosis of building models in dense urban areas from high resolution aerial images. Hence, we aim at reliably identifying roof facets that do not comply with quality specifications. The self-diagnosis process will highlight potential incorrect facets for their inspection by a human operator. A set of calibrated aerial images enable us to collect positive or negative evidences of roof facet existence and consistency. A particular attention has been paid to the definition of a set of low-level, complementary, robust and consistent image processing measures. Four quality classes have been defined and are used to classify roof facet quality. A supervised classifier and robust decision rules are then applied to perform an effective self-diagnosis according to the traffic light paradigm. Finally, the work in progress leads to a promising quantitative and qualitative evaluation in the context of dense urban areas. Numéro de notice : C2006-012 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE/POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : http://www.isprs.org/proceedings/XXXVI/part3/singlepapers/O_13.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86192 Documents numériques
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A supervised classification approach - pdf éditeurAdobe Acrobat PDF
Titre : Tree detection in aerial LIDAR and image data Type de document : Article/Communication Auteurs : John Secord, Auteur ; Avideh Zahkor, Auteur Editeur : New York [Etats-Unis] : IEEE Signal Processing Society Année de publication : 2006 Conférence : ICIP 2006, 13th IEEE International Conference on Image Processing 08/10/2006 11/10/2006 Atlanta Géorgie - Etats-Unis Proceedings IEEE Importance : 35 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne
[Termes IGN] modélisation 3D
[Termes IGN] segmentationRésumé : (auteur) In this paper, we present an approach to detecting trees in registered aerial image and range data obtained via LiDAR. The motivation for this problem comes from automated city modeling, in which such data is used to generate textured 3-D models. Representing the trees in these models is problematic because the data is usually too sparsely sampled in tree regions to create an accurate 3-D model of the trees. Furthermore, including the tree data points interferes with the polygonization step of the building roof top models. Therefore, it is advantageous to detect and remove points that represent trees in both LiDAR and aerial imagery. In this paper, we propose a two-step method for tree detection consisting of segmentation followed by classification. The segmentation is done using a simple region-growing algorithm using weighted features from aerial image and LiDAR, such as height, texture map, height variation, and normal vector estimates. The weights for the features are determined using a learning method on random walks. The classification is done using weighted support vector machines (SVM), allowing us to control the misclassification rate. The overall problem is formulated as a binary detection problem, and receiver operating characteristic curves are shown to validate our approach. Numéro de notice : C2006-024 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Communication DOI : 10.1109/ICIP.2006.312850 En ligne : https://doi.org/10.1109/ICIP.2006.312850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90963 On possible measures for evaluating the degree of uncertainty of fuzzy thematic maps / C. Ricotta in International Journal of Remote Sensing IJRS, vol 26 n° 24 (December 2005)
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Titre : On possible measures for evaluating the degree of uncertainty of fuzzy thematic maps Type de document : Article/Communication Auteurs : C. Ricotta, Auteur Année de publication : 2005 Article en page(s) : pp 5573 - 5583 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] carte thématique
[Termes IGN] classification floue
[Termes IGN] données maillées
[Termes IGN] entropie
[Termes IGN] incertitude des données
[Termes IGN] logique floue
[Termes IGN] modèle stochastique
[Termes IGN] pixelRésumé : (Auteur) In the remote sensing literature, a number of indices have been proposed for quantifying the uncertainty in categorical labelling of fuzzy thematic map locations. Most of these measures derive their conceptual basis from Shannon's entropy. Nonetheless, the Shannon entropy implies a probabilistic interpretation of class membership values, such that their application is appropriate only for fuzzy thematic maps obtained by softening the output of a maximum likelihood classification. There is therefore a need to derive measures of classification uncertainty for raster thematic maps obtained from nonprobabilistic soft classifiers. The purpose of this paper is to introduce a family of measures that are based on the notion of nonspecificity for quantifying the pixellevel categorical uncertainty associated to nonprobabilistic fuzzy classifications of remotely sensed images. Numéro de notice : A2005-524 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500285175 En ligne : https://doi.org/10.1080/01431160500285175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27660
in International Journal of Remote Sensing IJRS > vol 26 n° 24 (December 2005) . - pp 5573 - 5583[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05241 RAB Revue Centre de documentation En réserve L003 Exclu du prêt The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics / C. Zhu in International Journal of Remote Sensing IJRS, vol 26 n° 24 (December 2005)
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Titre : The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics Type de document : Article/Communication Auteurs : C. Zhu, Auteur ; Wei Shi, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 5493 - 5508 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse visuelle
[Termes IGN] appariement de formes
[Termes IGN] échelle d'intensité
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] image Ikonos
[Termes IGN] morphologie mathématique
[Termes IGN] niveau de gris (image)
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau routier
[Termes IGN] segment de droiteRésumé : (Auteur) With the development of remote sensors and satellite technologies, high-resolution satellite data such as IKONOS images have been available recently. By these new high resolution satellite data, remote sensing technologies can be successfully applied to more application areas such as extracting road network from high resolution satellite images. This paper proposes a newly developed approach to extract a road network from high resolution satellite images. The approach is based on the binary and greyscale mathematical morphology and a line segment match method. First, the outline of road network is detected based on the grey morphological characteristics. Then, the basic road network is detected by the line segment match method. Next, the detected basic road network is processed based on the knowledge about the roads and binary mathematical morphological methods. Finally, visual analysis and three indicators are used to evaluate the accuracy of the extracted road networks. The results of the accuracy evaluation demonstrate that the developed road network extraction approach can provide both good visual effect and high positional accuracy. Numéro de notice : A2005-523 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500300354 En ligne : https://doi.org/10.1080/01431160500300354 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27659
in International Journal of Remote Sensing IJRS > vol 26 n° 24 (December 2005) . - pp 5493 - 5508[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05241 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Integrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization / J.T. Morris in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)
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Titre : Integrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization Type de document : Article/Communication Auteurs : J.T. Morris, Auteur ; D. Porter, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 5221 - 5234 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Caroline du Sud (Etats-Unis)
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] données altimétriques
[Termes IGN] données lidar
[Termes IGN] image ADAR
[Termes IGN] image multibande
[Termes IGN] marais salant
[Termes IGN] niveau moyen des mers
[Termes IGN] North American Vertical Datum 1988
[Termes IGN] plante halophile
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sédimentation
[Termes IGN] système de référence géodésiqueRésumé : (Auteur) Vertical elevation relative to mean sea level is a critical variable for the productivity and stability of salt marshes. This research classified a high spatial resolution Airborne Data Acquisition and Registration (ADAR) digital camera image of a salt marsh landscape at North Inlet, South Carolina, USA using an artificial neural network. The remote sensing-derived thematic map was cross-referenced with Light Detection and Ranging (LIDAR) elevation data to compute the frequency distribution of marsh elevation relative to tidal elevations. At North Inlet, the median elevation of the salt marsh dominated by Spartina alterniflora was 0.349m relative to the North American Vertical Datum 1988 (NAVD88), while the mean high water level was 0.618m (2001 to May, 2003) with a mean tidal range of 1.39m. The distribution of elevations of Spartina habitat within its vertical range was normal, and 80% of the salt marsh was situated between a narrow range of 0.22 m and 0.481 m. Areas classified as Juncus marsh, dominated by Juneus roemerianus, had a broader, skewed distribution, with 80% of the distribution between 0.296 m and 0.981 m and a median elevation of 0.519 m. The Juneus marsh occurs within the intertidal region of brackish marshes and along the upper fringe of salt marshes. The relative elevation of the Spartina marsh at North Inlet is consistent with recent work that predicts a decrease in equilibrium elevation with an increasing rate of sea-level rise and suggests that the marshes here have not kept up with an increase in the rate of sea-level rise during the last two decades. Numéro de notice : A2005-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160500219018 En ligne : https://doi.org/10.1080/01431160500219018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27651
in International Journal of Remote Sensing IJRS > vol 26 n° 23 (December 2005) . - pp 5221 - 5234[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-05231 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Using satellite imagery and GIS for land-use and land-cover change mapping in an estuarine watershed / X. Yang in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)
PermalinkClassification morphologique du tissu urbain pour des applications climatologiques : cas de Marseille / N. Long in Revue internationale de géomatique, vol 15 n° 4 (décembre 2005 – février 2006)
PermalinkContribution de la télédétection au suivi de la sensibilité des sols à l'érosion à l'échelle d'un bassin versant (île de la Réunion) / V. Soti in Revue internationale de géomatique, vol 15 n° 4 (décembre 2005 – février 2006)
PermalinkReconstructing spatiotemporal trajectories from sparse data / P. Partsinevelos in ISPRS Journal of photogrammetry and remote sensing, vol 60 n° 1 (December 2005 - March 2006)
PermalinkA change detection model based on neighborhood correlation image analysis and decision tree classification / J. Im in Remote sensing of environment, vol 99 n° 3 (30/11/2005)
PermalinkQuantitative classification as a tool to show change in an urbanizing watershed / W.B. Clapham in International Journal of Remote Sensing IJRS, vol 26 n° 22 (November 2005)
PermalinkAssessment of ASTER land cover and MODIS data at multiple scales for ecological characterization of an arid urban center / W.L. Stefanov in Remote sensing of environment, vol 99 n° 1-2 (15 November 2005)
PermalinkIntegrating high resolution remote sensing, GIS and fuzzy set theory for identifying susceptibility areas of forest insect infestations / C. Bone in International Journal of Remote Sensing IJRS, vol 26 n° 21 (November 2005)
PermalinkSnow cover monitoring in Alpine regions using ENVISAT optical data / M. Pepe in International Journal of Remote Sensing IJRS, vol 26 n° 21 (November 2005)
PermalinkAménagement et développement rural dans le bas Loukkos (Maroc) : suivi et évaluation à l'aide de la télédetection et des SIG / R. Ragala in Photo interprétation, vol 41 n° 4 (Novembre 2005)
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