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Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 615 - 630 Titre : Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds Type de document : Article/Communication Auteurs : Longjie Ye, Auteur ; Ka Zhang, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 615 - 630 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme de filtrage [Termes IGN] classification barycentrique [Termes IGN] courbure [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fonction spline d'interpolation [Termes IGN] Kappa de Cohen [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de terrain [Termes IGN] processus gaussien [Termes IGN] semis de points Résumé : (Auteur) This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges. Numéro de notice : A2021-671 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.20-00080 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.87.20-00080 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98820 [article]

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Building Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples / Ka Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 4 (April 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 4 (April 2020) . - pp 235 - 245 Titre : Building Extraction from High-Resolution Remote Sensing Images Based on GrabCut with Automatic Selection of Foreground and Background Samples Type de document : Article/Communication Auteurs : Ka Zhang, Auteur ; Hui Chen, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 235 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] détection de contours [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] image Worldview [Termes IGN] segmentation d'image Résumé : (Auteur) This article proposes a new building extraction method from high-resolution remote sensing images, based on GrabCut, which can automatically select foreground and background samples under the constraints of building elevation contour lines. First the image is rotated according to the direction of pixel displacement calculated by the rational function Model. Second, the Canny operator, combined with morphology and the Hough transform, is used to extract the building's elevation contour lines. Third, seed points and interesting points of the building are selected under the constraint of the contour line and the geodesic distance. Then foreground and background samples are obtained according to these points. Fourth, GrabCut and geometric features are used to carry out image segmentation and extract buildings. Finally, WorldView satellite images are used to verify the proposed method. Experimental results show that the average accuracy can reach 86.34%, which is 15.12% higher than other building extraction methods. Numéro de notice : A2020-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.235 Date de publication en ligne : 01/04/2020 En ligne : https://doi.org/10.14358/PERS.86.4.235 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94797 [article]