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Object classification and recognition from mobile laser scanning point clouds in a road environment / Matti Lehtomäki in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 1226 - 1239 Titre : Object classification and recognition from mobile laser scanning point clouds in a road environment Type de document : Article/Communication Auteurs : Matti Lehtomäki, Auteur ; Anttoni Jaakkola, Auteur ; Juha Hyyppä, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1226 - 1239 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] classification automatique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] histogramme [Termes IGN] reconnaissance d'objets [Termes IGN] réseau routier [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) Automatic methods are needed to efficiently process the large point clouds collected using a mobile laser scanning (MLS) system for surveying applications. Machine-learning-based object recognition from MLS point clouds in a road and street environment was studied in order to create maps from the road environment infrastructure. The developed automatic processing workflow included the following phases: the removal of the ground and buildings, segmentation, segment classification, and object location estimation. Several novel geometry-based features, which were previously applied in autonomous driving and general point cloud processing, were applied for the segment classification of MLS point clouds. The features were divided into three sets, i.e., local descriptor histograms (LDHs), spin images, and general shape and point distribution features, respectively. These were used in the classification of the following roadside objects: trees, lamp posts, traffic signs, cars, pedestrians, and hoardings. The accuracy of the object recognition workflow was evaluated using a data set that contained more than 400 objects. LDHs and spin images were applied for the first time for machine-learning-based object classification in MLS point clouds in the surveying applications of the road and street environment. The use of these features improved the classification accuracy by 9.6% (resulting in 87.9% accuracy) compared with the accuracy obtained using 17 general shape and point distribution features that represent the current state of the art in the field of MLS; therefore, significant improvement in the classification accuracy was achieved. Connected component segmentation and ground extraction were the cause of most of the errors and should be thus improved in the future. Numéro de notice : A2016-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2476502 En ligne : https://doi.org/10.1109/TGRS.2015.2476502 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80000 [article]

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EuroSDR project Commission 2, Mobile mapping - road environment mapping using mobile laser scanning / Harri Kaartinen (2013)

Public contenu dans EuroSDR-Project, Commission 1 and Commission 2 / Eija Honkavaara (March 2013)     Titre : EuroSDR project Commission 2, Mobile mapping - road environment mapping using mobile laser scanning : final report Type de document : Chapitre/Contribution Auteurs : Harri Kaartinen, Auteur ; Juha Hyyppä, Auteur ; Antero Kukko, Auteur ; Matti Lehtomäki, Auteur ; Anttoni Jaakkola, Auteur ; M. George Vosselman, Auteur ; Sander J. Oude Elberink, Auteur ; Martin Rutzinger, Auteur ; Shi Pu, Auteur ; Matti Vaaja, Auteur Editeur : Dublin : European Spatial Data Research EuroSDR Année de publication : 2013 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] base de données routières [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] lasergrammétrie [Termes IGN] système de numérisation mobile [Termes IGN] télémétrie laser Note de contenu : 1. Introduction 2. State-of-the-art in mobile laser scanning 3. Benchmarking of mobile laser scanning systems on a test field 4. Benchmarking of pole detection algorithms 5. Discusion ans conclusions Numéro de notice : H2013-009 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Chapître / contribution Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75593

A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements / Anttoni Jaakkola in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 6 (November - December 2010)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 65 n° 6 (November - December 2010) . - pp 514 - 522 Titre : A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements Type de document : Article/Communication Auteurs : Anttoni Jaakkola, Auteur ; Antero Kukko, Auteur ; X. Yu, Auteur ; Juha Hyyppä, Auteur ; et al., Auteur Année de publication : 2010 Article en page(s) : pp 514 - 522 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] arbre (flore) [Termes IGN] capteur aérien [Termes IGN] chambre DTC [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drone [Termes IGN] géoréférencement direct [Termes IGN] GPS-INS [Termes IGN] lasergrammétrie [Termes IGN] lidar topographique [Termes IGN] rayonnement infrarouge thermique [Termes IGN] semis de points [Termes IGN] spectromètre [Termes IGN] système de numérisation mobile [Termes IGN] télémètre laser aéroporté [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) This paper presents a novel low-cost mini-UAV-based laser scanning system, which is also capable of performing car-based mobile mapping. The quality of the system and its feasibility for tree measurements was tested using the system’s laser scanner. The system was constructed as a modular measurement system consisting of a number of measurement instruments: a GPS/IMU positioning system, two laser scanners, a CCD camera, a spectrometer and a thermal camera. An Ibeo Lux and a Sick LMS151 profile laser were integrated into the system to provide dense point clouds; intensities of the reflected echoes can also be obtained with the Sick LMS. In our tests, when using a car as a platform, the pole-type object extraction algorithm which was developed resulted in 90% completeness and 86% correctness. The heights of pole-type objects were obtained with a bias of -1.6 cm and standard deviation of 5.4 cm. Using a mini-UAV as the platform, the standard deviation of individual tree heights was about 30 cm. Also, a digital elevation model extraction was tested with the UAV data, resulting in a height offset of about 3.1 cm and a standard deviation of 9.2 cm. With a multitemporal point cloud, we demonstrated a method to derive the biomass change of a coniferous tree with an R2 value of 0.92. The proposed system is capable of not only recording point cloud data giving the geometry of the objects, but also simultaneously collecting image data, including overlapping images and the intensity of laser backscatter, as well as hyperspectral and thermal data. Therefore we believe that the system is feasible for new algorithm and concept development and for basic research, especially when data is recorded multitemporally. Numéro de notice : A2010-484 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2010.08.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2010.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30677 [article]

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