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Auteur S. Clode |
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Building detection by fusion of airborne laser scanner data and multi-spectral images: performance evaluation and sensitivity analysis / Franz Rottensteiner in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)
[article]
Titre : Building detection by fusion of airborne laser scanner data and multi-spectral images: performance evaluation and sensitivity analysis Type de document : Article/Communication Auteurs : Franz Rottensteiner, Auteur ; John C. Trinder, Auteur ; S. Clode, Auteur ; K. Kubik, Auteur Année de publication : 2007 Article en page(s) : pp 135 - 149 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse de sensibilité
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] fusion de données multisource
[Termes IGN] image multibande
[Termes IGN] lasergrammétrie
[Termes IGN] test de performanceRésumé : (Auteur) In this paper, we describe the evaluation of a method for building detection by the Dempster–Shafer fusion of airborne laser scanner (ALS) data and multi-spectral images. For this purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic models for the probability mass assignments are validated and improved, and rules for tuning the parameters are discussed. The sensitivity of the results to the most important control parameters of the method is assessed. Further we evaluate the contributions of the individual cues used in the classification process to determine the quality of the results. Applying our method with a standard set of parameters on two different ALS data sets with a spacing of about 1 point/m2, 95% of all buildings larger than 70 m2 could be detected and 95% of all detected buildings larger than 70 m2 were correct in both cases. Buildings smaller than 30 m2 could not be detected. The parameters used in the method have to be appropriately defined, but all except one (which must be determined in a training phase) can be determined from meaningful physical entities. Our research also shows that adding the multi-spectral images to the classification process improves the correctness of the results for small residential buildings by up to 20%. Copyright ISPRS Numéro de notice : A2007-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.03.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.03.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28624
in ISPRS Journal of photogrammetry and remote sensing > vol 62 n° 2 (June 2007) . - pp 135 - 149[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-07041 SL Revue Centre de documentation Revues en salle Disponible Detection and vectorization of roads from lidar data / S. Clode in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 5 (May 2007)
[article]
Titre : Detection and vectorization of roads from lidar data Type de document : Article/Communication Auteurs : S. Clode, Auteur ; Franz Rottensteiner, Auteur ; P. Kootsookos, Auteur ; E. Zelniker, Auteur Année de publication : 2007 Article en page(s) : pp 517 - 535 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Australie
[Termes IGN] axe médian
[Termes IGN] convolution (signal)
[Termes IGN] détection automatique
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] extraction du réseau routier
[Termes IGN] lasergrammétrie
[Termes IGN] milieu urbain
[Termes IGN] réseau routier
[Termes IGN] semis de points
[Termes IGN] vectorisationRésumé : (Auteur) A method for the automatic detection and vectorization of roads from lidar data is presented. To extract roads from a lidar point cloud, a hierarchical classification technique is used to classify the lidar points progressively into road and non-road points. During the classification process, both intensity and height values are initially used. Due to the homogeneous and consistent nature of roads, a local point density is introduced to finalize the classification. The resultant binary classification is then vectorized by convolving a complex-valued disk named the Phase Coded Disk (PCD) with the image to provide three separate pieces of information about the road. The centerline and width of the road are obtained from the resultant magnitude image while the direction is determined from the corresponding phase image, thus completing the vectorized road model. All algorithms used are described and applied to two urban test sites. Completeness values of 0.88 and 0.79 and correctness values of 0.67 and 0.80 were achieved for the classification phase of the process. The vectorization of the classified results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81 and correctness values of 0.75 and 0.80 for two different data sets. Copyright ASPRS Numéro de notice : A2007-244 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.5.517 En ligne : https://doi.org/10.14358/PERS.73.5.517 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28607
in Photogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 5 (May 2007) . - pp 517 - 535[article]