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Planar polygons detection in lidar scans based on sensor topology enhanced Ransac / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 343 - 350 Titre : Planar polygons detection in lidar scans based on sensor topology enhanced Ransac Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Zoumana Mallé, Auteur ; Oussama Ennafii , Auteur ; Pascal Monasse, Auteur ; Bruno Vallet , Auteur Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Commanditaire Année de publication : 2020 Projets : Biom / Vallet, Bruno Article en page(s) : pp 343 - 350 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] polygone [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] topologie capteur [Termes descripteurs IGN] traitement de nuage de points [Termes descripteurs IGN] transformation de Hough Résumé : (auteur) Detecting planar structures in point clouds is a very central step of the point cloud processing pipeline as many Lidar scans, in particular in anthropic environments, present such planar structures. Many improvements have been proposed to RANSAC and the Hough transform, the two major types of plane detection methods. An important limitation however is that these methods detect planes running across the whole scene instead of more localized planar patches. Moreover, they do not exploit the sensor information that often comes with Lidar point cloud (sensor topology and optical center position in particular). In this paper we address both issues: we aim at detecting planar polygons that have a limited spatial extent, and we exploit sensor topology. The latter is used to enhance a RANSAC framework on two aspects: to make seed points selection more local and to define more compact sets of inliers through sensor space region growing. Numéro de notice : A2020-502 Affiliation des auteurs : UMR LaSTIG+Ext Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-343-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-343-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95643 [article]

Delineating minor landslide displacements using GPS and terrestrial laser scanning-derived terrain surfaces and trees: a case study of the Slumgullion landslide, Lake City, Colorado / Jin Wang in Survey review, vol 52 n° 372 (May 2020)  

Public [article]  inSurvey review > vol 52 n° 372 (May 2020) . - pp 215 - 223 Titre : Delineating minor landslide displacements using GPS and terrestrial laser scanning-derived terrain surfaces and trees: a case study of the Slumgullion landslide, Lake City, Colorado Type de document : Article/Communication Auteurs : Jin Wang, Auteur ; Duo Wang, Auteur ; Shengqi Liu, Auteur ; Boyu Jia, Auteur Année de publication : 2020 Article en page(s) : pp 215 - 223 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] algorithme ICP [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] arbre (flore) [Termes descripteurs IGN] Colorado (Etats-Unis) [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télémétrie laser terrestre Résumé : (Auteur) Multi-temporal high-density terrestrial laser scanning (TLS) datasets are processed to delineating possible movements from terrain surfaces and trees. Terrain surface movements are estimated with the help of segmentation and random sample consensus (RANSAC) algorithm. Tree movements are interpreted by iterative closest point (ICP) solved translations and rotations of tree point clouds. The capabilities of the proposed methodology were tested using a case study of the Slumgullion landslide, where the trees without clear trunks cover the terrain surfaces. The displacements from the terrain surfaces and trees are similar with the results observed using our global positioning system (GPS) and historic results. Numéro de notice : A2020-177 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1558580 date de publication en ligne : 25/12/2018 En ligne : https://doi.org/10.1080/00396265.2018.1558580 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94835 [article]

An improved RANSAC algorithm for extracting roof planes from airborne lidar data / Sibel Canaz Sevgen in Photogrammetric record, vol 35 n° 169 (March 2020)  

Public [article]  inPhotogrammetric record > vol 35 n° 169 (March 2020) . - pp 40 - 57 Titre : An improved RANSAC algorithm for extracting roof planes from airborne lidar data Type de document : Article/Communication Auteurs : Sibel Canaz Sevgen, Auteur ; Fevzi Karsli, Auteur Année de publication : 2020 Article en page(s) : pp 40 - 57 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Algorithmique [Termes descripteurs IGN] bord décollé (toit) [Termes descripteurs IGN] contrôle qualité [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] segmentation par croissance de régions [Termes descripteurs IGN] semis de points Résumé : (Auteur) The extraction of building roof planes from lidar data has become a popular research topic with random sample consensus (RANSAC) being one of the most commonly adopted algorithms. RANSAC extracts full planes, which is problematic when there are other points outside the plane boundary but within the plane space. This study proposes an improved RANSAC (I‐RANSAC) algorithm by removing points that do not belong to the roof plane. I‐RANSAC selects a random point from the extracted roof plane and then searches for its neighbours within a given threshold to identify and remove outliers. The new algorithm was tested with 14 buildings from two datasets, where quality control measures showed significant improvement over standard RANSAC. Numéro de notice : A2020-131 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12296 date de publication en ligne : 13/11/2019 En ligne : https://doi.org/10.1111/phor.12296 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94815 [article]

Reducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 177 - 186 Titre : Reducing shadow effects on the co-registration of aerial image pairs Type de document : Article/Communication Auteurs : Matthew Plummer, Auteur ; Douglas A. Stow, Auteur ; Emmanuel Storey, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 177 - 186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de données [Termes descripteurs IGN] correction des ombres [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] effet d'ombre [Termes descripteurs IGN] enregistrement de données [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] intensité lumineuse [Termes descripteurs IGN] masque [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] SIFT (algorithme) Résumé : (auteur) Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows. Numéro de notice : A2020-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.177 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.4.177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94776 [article]

Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 13 - 25 Titre : Automatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios Type de document : Article/Communication Auteurs : Klemen Istenič, Auteur ; Nuno Gracias, Auteur ; Aurélien Arnaubec, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 13 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] étalonnage [Termes descripteurs IGN] faisceau laser [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image sous-marine [Termes descripteurs IGN] photogrammétrie sous-marine [Termes descripteurs IGN] Ransac (algorithme) [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] structure-from-motion Résumé : (Auteur) Improvements in structure-from-motion techniques are enabling many scientific fields to benefit from the routine creation of detailed 3D models. However, for a large number of applications, only a single camera is available for the image acquisition, due to cost or space constraints in the survey platforms. Monocular structure-from-motion raises the issue of properly estimating the scale of the 3D models, in order to later use those models for metrology. The scale can be determined from the presence of visible objects of known dimensions, or from information on the magnitude of the camera motion provided by other sensors, such as GPS. This paper addresses the problem of accurately scaling 3D models created from monocular cameras in GPS-denied environments, such as in underwater applications. Motivated by the common availability of underwater laser scalers, we present two novel approaches which are suitable for different laser scaler configurations. A fully unconstrained method enables the use of arbitrary laser setups, while a partially constrained method reduces the need for calibration by only assuming parallelism on the laser beams and equidistance with the camera. The proposed methods have several advantages with respect to existing methods. By using the known geometry of the scene represented by the 3D model, along with some parameters of the laser scaler geometry, the need for laser alignment with the optical axis of the camera is eliminated. Furthermore, the extremely error-prone manual identification of image points on the 3D model, currently required in image-scaling methods, is dispensed with. The performance of the methods and their applicability was evaluated both on data generated from a realistic 3D model and on data collected during an oceanographic cruise in 2017. Three separate laser configurations have been tested, encompassing nearly all possible laser setups, to evaluate the effects of terrain roughness, noise, camera perspective angle and camera-scene distance on the final estimates of scale. In the real scenario, the computation of 6 independent model scale estimates using our fully unconstrained approach, produced values with a standard deviation of 0,3 %. By comparing the values to the only other possible method currently usable for this dataset, we showed that the consistency of scales obtained for individual lasers is much higher for our approach (0,6 % compared to 4 %). Numéro de notice : A2020-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.10.007 date de publication en ligne : 14/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.10.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94397 [article]

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