Centre dedocumentation
scientifique

Automated extraction of buildings from Ikonos imagery by integrating spectral and spatial information / X. Wang in Geomatica, vol 63 n° 3 (September 2009)

Public [article]  inGeomatica > vol 63 n° 3 (September 2009) . - 10 p. ; pp 193 - 202 Titre : Automated extraction of buildings from Ikonos imagery by integrating spectral and spatial information Type de document : Article/Communication Auteurs : X. Wang, Auteur ; J. Li, Auteur ; Y. Li, Auteur Année de publication : 2009 Article en page(s) : 10 p. ; pp 193 - 202 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classe d'objets [Termes IGN] classification non dirigée [Termes IGN] détection du bâti [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image Ikonos [Termes IGN] objet homogène [Termes IGN] reconstruction 2D du bâti [Termes IGN] segmentation d'image [Termes IGN] toit Résumé : (Auteur) Cet article présente une nouvelle approche à l'extraction de bâtiments pour la détection et l'extraction de contours à deux dimensions (2-D) des toits de bâtiments à partir de l'imagerie (pan-sharpened) couleur IKONOS en utilisant des algorithmes améliorés d'intégration spectrale et spatiale. En se basant sur l'algorithme d'extraction et de classification des objets homogènes (ECHO) et sur l'algorithme d'extraction et de classification non supervisées des objets homogènes (UnECHO), trois nouveaux algorithmes pour l'extraction des bâtiments sont proposés. Il s'agit de la segmentation supervisée à multisommets (SSMS), l'analyse du voisinage couvert (AVC) et la détection de la structure transvoisinage (DSTV). Les étapes fondamentales de l'approche proposée sont les suivantes : (1) la segmentation spectrale par la SSMS, (2) l'intégration de l'information spectrale et spatiale par l'AVC et la DSTV et (3) la délimitation du contour des toits des bâtiments. La performance de l'approche proposée est évaluée en réalisant des essais des différentes scènes des images IKONOS et en la comparant avec d'autres algorithmes. Copyright Geomatica Numéro de notice : A2009-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30087 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
035-09031	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Classification of roof materials for rainwater pollution modelization / Arnaud Le Bris (2009)    

Public Titre : Classification of roof materials for rainwater pollution modelization Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Pauline Robert-Sainte, Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2009 Conférence : ISPRS 2009, High-Resolution Earth Imaging for Geospatial Information workshop 02/06/2009 05/06/2009 Hanovre Allemagne OA ISPRS Archives Importance : 6 p. Format : 21 x 30 cm Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] BD Topo [Termes IGN] classification dirigée [Termes IGN] eau pluviale [Termes IGN] image RVB [Termes IGN] matériau [Termes IGN] milieu urbain [Termes IGN] ombre [Termes IGN] orthoimage couleur [Termes IGN] pollution des eaux [Termes IGN] segmentation d'image [Termes IGN] toit Résumé : (Auteur) It has been proven that roof runoff water plays an important role in the high metallic concentration levels in urban rainwater since metallic elements are generated by corrosion of roof materials before being swept away by rainwater. The aim of TOITEAU project is therefore to model this phenomenon, evaluating the metallic flows from roofs in rainwater. To achieve this goal, an important work has already been done to model those flows at roof scale. But, it has now to be extrapolated to a whole drainage area, requiring knowledge about the areas concerned by the different kinds of roof coverage, that is to say that a map of roof materials is needed. Such information can be extracted from aerial (ortho) images owing to (supervised) classification techniques. In the present situation, only six classes corresponding to the following kinds of roofs were defined : zinc plates, slates, red tiles, brown tiles and flat roofs. Nevertheless, classification results are limited because of several factors that have therefore to be dealt with. First of all, some distinct classes have very similar radiometric distribution (such as for instance zinc and at light slates), making it hard to distinguish between them. That's why derived channels computed from initial red-green-blue channels of the ortho-image have been used to improve the classification results. Texture channels have also been tested especially to discriminate zinc from other light coloured roof materials. For the same reason and in order not to obtain a too ”noisy” result, per region classification algorithms have been used : homogeneous regions will be classified instead of pixels. Secondly, roofs are the only interesting parts of the ortho-image in this study. As a consequence, a building mask is first computed from digital topographic database BDTopo in order to classify only roofs. However, several elements concerning data precision have to be taken into account at this step. For instance, the ortho-image and the topographic database can obviously not have been captured at the same date and, as a consequence, buildings can have been destroyed, modified or built between these two distinct capture times. In addition, as the used ortho-image is not a ”true ortho-image”, building objects from digital topographic database and ortho-image roofs are not perfectly superposed. However, these topographic database building objects can be registered to the ortho-image. Nevertheless, it must be said that these database objects often remain caricatures of true buildings. Besides, most of the time, homogeneous regions to be classified do not directly correspond to database buildings since those database objects can be groups of buildings or buildings of which the roof is composed of different materials. Therefore, it is necessary to segment building areas (according to the topographic database) of the ortho-image into homogeneous regions that are then classified. Lastly, shadows can be quite important in roof areas because of the presence of roof superstructures or higher buildings in the neighbourhood. That's why an additional class ”shadow” is also defined in order to take into account shadow areas where radiometric information is not sufficient to discriminate between the different kinds of materials. Tests have been carried out on two distinct study areas with 50cm resolution orthophotos for the first one and 12cm resolution orthoimages for the second one. The first study area was a dense urban centre, whereas the second could be divided into several parts : a residential suburb consisting of houses, a dense urban centre with buildings having up to 4-5 levels and a mixed residential / service area consisting of higher buildings. Numéro de notice : C2009-038 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://www.isprs.org/proceedings/XXXVIII/1_4_7-W5/paper/LE_BRIS-152.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=64298

Documents numériques

en open access Classification of roof materials ... - pdf éditeur Adobe Acrobat PDF

Topographic laser ranging and scanning, ch 14. Feature extraction from lidar data in urban areas / Frédéric Bretar (2009)

Public contenu dans Topographic laser ranging and scanning / Jie Shan (2009)   Titre de série : Topographic laser ranging and scanning, ch 14 Titre : Feature extraction from lidar data in urban areas Type de document : Chapitre/Contribution Auteurs : Frédéric Bretar, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2009 Importance : pp 403 - 419 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Ransac (algorithme) [Termes IGN] toit [Termes IGN] transformation de Hough [Termes IGN] zone urbaine Résumé : (auteur) This chapter focuses on the extraction of three-dimensional (3D) planar primitives. It presents two approaches for detecting 3D roof facets; the first one is based on the analysis of the 3D point cloud while the second one integrates aerial images. The extraction of lines from a Light Detection and Ranging (LiDAR) point cloud is a difficult task since LiDAR points are randomly distributed over surfaces: depending on the point density, building edges are approximately delineated. Integrating aerial images with LiDAR data in a primitive detection process may highly enhance the resulting facets. Automatic mapping of urban areas from aerial images is a challenging task for scientists and surveyors because of the complexity of urban scenes. The chapter shows that searching for planar primitives in a LiDAR point cloud has limitations with regard to a joint use of aerial images and LiDAR data. Numéro de notice : H2009-008 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102233

A Polygonal approach for automation in extraction of serial modular roofs / Y. Avrahami in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 11 (November 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 11 (November 2008) . - pp 1365 - 1378 Titre : A Polygonal approach for automation in extraction of serial modular roofs Type de document : Article/Communication Auteurs : Y. Avrahami, Auteur ; Y. Raizman, Auteur ; Y. Doytsher, Auteur Année de publication : 2008 Article en page(s) : pp 1365 - 1378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] base de connaissances [Termes IGN] classificateur paramétrique [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] polygone [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit [Termes IGN] visualisation 3D Résumé : (Auteur) This paper presents a novel approach for automation in roof extraction from two solved aerial images. The approach assumes that roofs are composed of several spatial polygons, and that they can be obtained by extracting all or even only some of them if the model is known. In view of this assumption, innovative algorithms for semi-automatic spatial polygon extraction were developed. These algorithms are based on a 2D approach to solving the 3D reality. Based on these algorithms, an interactive and semi-automatic modelbased approach for automation in roof extraction was developed. The approach is composed of two phases: manual (interactive) and automatic. In the manual (interactive) phase, the operator needs to choose an Expanded Parameterized Model (EPM) from a knowledge base and select one pre-prepared Interactive Option for Extraction (IOE) of the roof. Then, the operator needs to point according to the guidelines of the chosen option in the left image space. In the automatic phase, the selected spatial polygons are extracted, the parameters of the selected model are calculated and the roof is reconstructed. The approach was examined and the results we obtained had standard accuracy. It appears that the approach can be implemented on many types of roofs and under diverse photographic conditions. In this paper, the algorithms, the experiments and the results are detailed. Copyright ASPRS Numéro de notice : A2008-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.11.1365 En ligne : https://doi.org/10.14358/PERS.74.11.1365 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29401 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-08111	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
105-08112	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Using a binary space partitioning tree for reconstructing polyhedral building models from airborne Lidar data / Gunho Sohn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 11 (November 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 11 (November 2008) . - pp 1425 - 1438 Titre : Using a binary space partitioning tree for reconstructing polyhedral building models from airborne Lidar data Type de document : Article/Communication Auteurs : Gunho Sohn, Auteur ; X. Huang, Auteur ; V. Tao, Auteur Année de publication : 2008 Article en page(s) : pp 1425 - 1438 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre-B [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation 3D [Termes IGN] polyèdre [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit Résumé : (Auteur) During the past several years, point density covering topographic objects with airborne lidar (Light Detection And Ranging) technology has been greatly improved. This achievement provides an improved ability for reconstructing more complicated building roof structures; more specifically, those comprising various model primitives horizontally and/or vertically. However, the technology for automatically reconstructing such a complicated structure is thus far poorly understood and is currently based on employing a limited number of pre-specified building primitives. This paper addresses this limitation by introducing a new technique of modeling 3D building objects using a data-driven approach whereby densely collecting low-level modeling cues from lidar data are used in the modeling process. The core of the proposed method is to globally reconstruct geometric topology between adjacent linear features by adopting a BSP (Binary Space Partitioning) tree. The proposed algorithm consists of four steps: (a) detecting individual buildings from lidar data, (b) clustering laser points by height and planar similarity, (c) extracting rectilinear lines, and (d) planar partitioning and merging for the generation of polyhedral models. This paper demonstrates the efficacy of the algorithm for creating complex models of building rooftops in 3D space from airborne lidar data. Copyright ASPRS Numéro de notice : A2008-410 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.11.1425 En ligne : https://doi.org/10.14358/PERS.74.11.1425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29402 [article]

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-08111	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
105-08112	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

Videogrammetric monitoring of as-built membrane roof structures / S.Y. Lin in Photogrammetric record, vol 23 n° 122 (June - August 2008)  

Permalink

Photogrammetric and LIDAR data integration using the centroid of a rectangular roof as a control point / Edson Aparecido Mitishita in Photogrammetric record, vol 23 n° 121 (March - May 2008)  

Permalink

An efficient approach to building superstructure reconstruction using digital elevation maps / Fadi Dornaika (2008)  

Permalink

A stochastic framework for the identification of building rooftops using a single remote sensing image / A. Katartzis in IEEE Transactions on geoscience and remote sensing, vol 46 n° 1 (January 2008)  

Permalink

Construction et intégration de maquettes 3D dans un SIG / M. Koehl in Géomatique expert, n° 58 (01/09/2007)

Permalink

Semi-automatic approach toward mapping of flat-roofed buildings within a non-stereoscopic environment / Y. Avrahami in Photogrammetric record, vol 22 n° 117 (March - May 2007)  

Permalink

3D building reconstruction with parametric roof superstructures / Mathieu Brédif (2007)  

Permalink

Détection et reconstruction de facettes 3D par approche hiérarchique par régions, à partir de couples d'images satellite THR / Nesrine Chehata in Bulletin d'information scientifique et technique de l'IGN, n° 75 (mars 2006)

Permalink

Automatic building reconstruction from a digital elevation model and cadastral data : an operational approach / Mélanie Durupt (2006)    

Permalink

A supervised classification approach towards quality self-diagnosis of 3D building models using digital aerial imagery / Laurence Boudet (2006)    

Permalink