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Deep learning for conifer / deciduous classification of airborne LiDAR 3D point clouds representing individual trees / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) . - pp 219 - 230 Titre : Deep learning for conifer / deciduous classification of airborne LiDAR 3D point clouds representing individual trees Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Nathan B. Jacobs, Auteur ; Marco A. Contreras, Auteur ; Chase H. Clark, Auteur Année de publication : 2019 Article en page(s) : pp 219 - 230 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] houppier [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] pinophyta [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] télédétection par lidar Résumé : (auteur) The purpose of this study was to investigate the use of deep learning for coniferous/deciduous classification of individual trees segmented from airborne LiDAR data. To enable processing by a deep convolutional neural network (CNN), we designed two discrete representations using leaf-off and leaf-on LiDAR data: a digital surface model with four channels (DSM × 4) and a set of four 2D views (4 × 2D). A training dataset of tree crowns was generated via segmentation of tree crowns, followed by co-registration with field data. Potential mislabels due to GPS error or tree leaning were corrected using a statistical ensemble filtering procedure. Because the training data was heavily unbalanced (~8% conifers), we trained an ensemble of CNNs on random balanced sub-samples. Benchmarked against multiple traditional shallow learning methods using manually designed features, the CNNs improved accuracies up to 14%. The 4 × 2D representation yielded similar classification accuracies to the DSM × 4 representation (~82% coniferous and ~90% deciduous) while converging faster. Further experimentation showed that early/late fusion of the channels in the representations did not affect the accuracies in a significant way. The data augmentation that was used for the CNN training improved the classification accuracies, but more real training instances (especially coniferous) likely results in much stronger improvements. Leaf-off LiDAR data were the primary source of useful information, which is likely due to the perennial nature of coniferous foliage. LiDAR intensity values also proved to be useful, but normalization yielded no significant improvement. As we observed, large training data may compensate for the lack of a subset of important domain data. Lastly, the classification accuracies of overstory trees (~90%) were more balanced than those of understory trees (~90% deciduous and ~65% coniferous), which is likely due to the incomplete capture of understory tree crowns via airborne LiDAR. In domains like remote sensing and biomedical imaging, where the data contain a large amount of information and are not friendly to human visual system, human-designed features may become suboptimal. As exemplified by this study, automatic, objective derivation of optimal features via deep learning can improve prediction tasks in such domains. Numéro de notice : A2019-547 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.10.011 date de publication en ligne : 03/11/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94192 [article]

Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes / Antonio Tomás Mozas-Calvache in International journal of geographical information science IJGIS, vol 33 n°11 (November 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 33 n°11 (November 2019) . - pp 2170 - 2187 Titre : Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes Type de document : Article/Communication Auteurs : Antonio Tomás Mozas-Calvache, Auteur ; Francisco Javier Ariza-López, Auteur Année de publication : 2019 Article en page(s) : pp 2170 - 2187 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes descripteurs IGN] axe médian [Termes descripteurs IGN] contenu généré par les utilisateurs [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] réseau routier [Termes descripteurs IGN] trace GPS [Termes descripteurs IGN] vitesse de déplacement Résumé : (auteur) The sharing of Global Navigation Satellite System (GNSS) tracks on the Internet is increasing enormously. Every day a great number of users capture routes using different devices and share these data. Individually these tracks present a poor positional accuracy because these devices obtain positions with accuracy of about 5-10 metres. In addition, they are usually captured for navigation and not for surveying. However, we can take advantage of the great quantity of tracks of the same linear element in order to obtain a more accurate solution. This study analyses this possibility using a wide set of tracks obtained in known conditions. We emulated those tracks obtained by Volunteered Geographic Information (VGI) users and we compared the mean axis obtained using all tracks with others obtained from a more accurate source. Additionally, we analyse the displacement of other axes obtained by varying several parameters such as the number of tracks and their length or by dividing the route into sections in function of sinuosity, etc. The results have shown an improved 3D mean axis and the viability of the method proposed in this study in order to use axes obtained from several tracks in maps at certain scales. Numéro de notice : A2019-429 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1645335 date de publication en ligne : 26/07/2019 En ligne : https://doi.org/10.1080/13658816.2019.1645335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93563 [article]

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Semiautomatically register MMS LiDAR points and panoramic image sequence using road lamp and lane / Ningning Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 11 (November 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 11 (November 2019) . - pp 829 - 840 Titre : Semiautomatically register MMS LiDAR points and panoramic image sequence using road lamp and lane Type de document : Article/Communication Auteurs : Ningning Zhu, Auteur ; Yonghong Jia, Auteur ; Xia Huang, Auteur Année de publication : 2019 Article en page(s) : pp 829 - 840 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] éclairage public [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image panoramique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] séquence d'images [Termes descripteurs IGN] système de numérisation mobile Résumé : (Auteur) We propose using the feature points of road lamp and lane to register mobile mapping system (MMS) LiDAR points and panoramic image sequence. Road lamp and lane are the common sobjects on roads; the spatial distributions are regular, and thus our registration method has wide applicability and high precision. First, the road lamp and lane were extracted from the LiDAR points by horizontal grid and reflectance intensity and then by optimizing the endpoints as the feature points of road lamp and lane. Second, the feature points were projected onto the panoramic image by initial parameters and then by extracting corresponding feature points near the projection location. Third, the direct linear transformation method was used to solve the registration model and eliminate mismatching feature points. In the experiments, we compare the accuracy of our registration method with other registration methods by a sequence of panoramic images. The results show that our registration method is effective; the registration accuracy of our method is less than 10 pixels and averaged 5.84 pixels in all 31 panoramic images (4000 × 8000 pixels), which is much less than that of the 56.24 pixels obtained by the original registration method. Numéro de notice : A2019-525 Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.14358/PERS.85.11.829 date de publication en ligne : 01/11/2019 En ligne : https://doi.org/10.14358/PERS.85.11.829 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94062 [article]

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Postprocessing synchronization of a laser scanning system aboard a UAV / Marcela do Valle Machado in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 753 - 763 Titre : Postprocessing synchronization of a laser scanning system aboard a UAV Type de document : Article/Communication Auteurs : Marcela do Valle Machado, Auteur ; Antonio Maria Garcia Tommaselli, Auteur ; Fernanda Magri Torres, Auteur ; Mariana Batista Campos, Auteur Année de publication : 2019 Article en page(s) : pp 753 - 763 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] décalage d'horloge [Termes descripteurs IGN] données altimétriques [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] positionnement inertiel [Termes descripteurs IGN] post-traitement [Termes descripteurs IGN] précision altimétrique [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] signal GNSS [Termes descripteurs IGN] synchronisation [Termes descripteurs IGN] télémétrie laser aéroporté [Termes descripteurs IGN] trajectoire Résumé : (Auteur) Synchronization of airborne laser scanning devices is a critical process that directly affects data accuracy. This process can be more challenging with low-cost airborne laser scanning (ALS) systems because some device connections from off-the-shelf sensors are less stable. An alternative to synchronization is performing a postprocessing clock correction. This article presents a technique for postprocessing synchronization (off-line) that estimates clock differences based on the correlation between the signals from the global navigation satellite system (GNSS) trajectory and the light detection and ranging (lidar) range, followed by refinement with a least-squares method. The correlation between signals was automatically estimated considering the planned flight maneuvers, in a flat terrain, to produce altimetric trajectory variations. Experiments were performed with an Ibeo LUX laser unit integrated with a NovAtel SPAN-IGM-S1 inertial navigation system that was transported by an unmanned aerial vehicle (UAV). The planimetric and altimetric accuracies of the point cloud obtained with the proposed postprocessing synchronization technique were 28 cm and 10 cm, respectively, at a flight height of 35 m. Numéro de notice : A2019-523 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.753 date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.753 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93994 [article]

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sUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (1 October 2019)  

Public [article]  inRemote sensing > vol 11 n° 19 (1 October 2019) . - 19 p. Titre : sUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar Type de document : Article/Communication Auteurs : Paul J. Kinzel, Auteur ; Carl J. Legleiter, Auteur Année de publication : 2019 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] bathymétrie laser [Termes descripteurs IGN] Colorado (Etats-Unis) [Termes descripteurs IGN] débit [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] lidar à effet Doppler [Termes descripteurs IGN] Matlab [Termes descripteurs IGN] rivière [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] télédétection acoustique [Termes descripteurs IGN] vitesse Résumé : (auteur) This paper describes a non-contact methodology for computing river discharge based on data collected from small Unmanned Aerial Systems (sUAS). The approach is complete in that both surface velocity and channel geometry are measured directly under field conditions. The technique does not require introducing artificial tracer particles for computing surface velocity, nor does it rely upon the presence of naturally occurring floating material. Moreover, no prior knowledge of river bathymetry is necessary. Due to the weight of the sensors and limited payload capacities of the commercially available sUAS used in the study, two sUAS were required. The first sUAS included mid-wave thermal infrared and visible cameras. For the field evaluation described herein, a thermal image time series was acquired and a particle image velocimetry (PIV) algorithm used to track the motion of structures expressed at the water surface as small differences in temperature. The ability to detect these thermal features was significant because the water surface lacked floating material (e.g., foam, debris) that could have been detected with a visible camera and used to perform conventional Large-Scale Particle Image Velocimetry (LSPIV). The second sUAS was devoted to measuring bathymetry with a novel scanning polarizing lidar. We collected field measurements along two channel transects to assess the accuracy of the remotely sensed velocities, depths, and discharges. Thermal PIV provided velocities that agreed closely ( R2 = 0.82 and 0.64) with in situ velocity measurements from an acoustic Doppler current profiler (ADCP). Depths inferred from the lidar closely matched those surveyed by wading in the shallower of the two cross sections ( R2 = 0.95), but the agreement was not as strong for the transect with greater depths ( R2 = 0.61). Incremental discharges computed with the remotely sensed velocities and depths were greater than corresponding ADCP measurements by 22% at the first cross section and Numéro de notice : A2019-554 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11192317 date de publication en ligne : 05/10/2019 En ligne : https://doi.org/10.3390/rs11192317 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94207 [article]

Transferability and calibration of airborne laser scanning based mixed-effects models to estimate the attributes of sawlog-sized Scots pines / Lauri Korhonen in Silva fennica, vol 53 n° 3 (2019)  

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Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

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Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

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Delineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)  

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Enhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences / Bo Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)  

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Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)  

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PpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? / Sandra Bujan in Photogrammetric record, vol 34 n° 167 (September 2019)  

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Reduction of measurement data before Digital Terrain Model generation vs. DTM generalisation / Wioleta Błaszczak-Bąk in Survey review, vol 51 n° 368 (September 2019)  

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Topographie et archéologie, du cordeau au tout numérique : plus de 40 ans d'interactions / Bertrand Chazaly in XYZ, n° 160 (septembre 2019)

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Validating the use of object-based image analysis to map commonly recognized landform features in the United States / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 46 n° 5 (September 2019)  

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