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Learning effectiveness of virtual environments for 3D terrain interpretation and data acquisition / A.M. Perez-Romero in Survey review, vol 49 n° 355 (October 2017)
[article]
Titre : Learning effectiveness of virtual environments for 3D terrain interpretation and data acquisition Type de document : Article/Communication Auteurs : A.M. Perez-Romero, Auteur ; M. Castro-Garcia, Auteur ; M.J. Leon-Bonillo, Auteur ; F. Manzano-Agugliaro, Auteur Année de publication : 2017 Article en page(s) : pp 302 - 311 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Formation
[Termes IGN] données localisées 3D
[Termes IGN] modèle physique
[Termes IGN] monde virtuelRésumé : (Auteur) The aim of this study is to assess the effectiveness of different learning strategies for 3D terrain interpretation and data acquisition by engineering students. According to the experimental design, students received homogeneous training, followed by differential training, which divided the students into three statistically homogeneous groups where each group was subject to a different learning process: (1) virtual environment learning; (2) learning using physical scale models; and (3) a theoretical class. Afterwards, the students were evaluated using two tests under real field conditions. Results were obtained for the following study variables: field-test scores and whether or not the student was repeating the course. The students who learned using physical scale models obtained the best scores; their scores were significantly higher than those of students using virtual environment or a theoretical class. These findings open up new perspectives on the teaching of surveying with respect to other teaching methods. Numéro de notice : A2017-553 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1080/00396265.2016.1172814 En ligne : https://doi.org/10.1080/00396265.2016.1172814 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86614
in Survey review > vol 49 n° 355 (October 2017) . - pp 302 - 311[article]Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data / Hooman Latifi in Forestry, an international journal of forest research, vol 90 n° 4 (October 2017)
[article]
Titre : Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data Type de document : Article/Communication Auteurs : Hooman Latifi, Auteur ; Steven Hill, Auteur ; Bastian Schumann, Auteur ; Marco Heurich, Auteur ; Stefan Dech, Auteur Année de publication : 2017 Article en page(s) : pp 496 - 514 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] forêt tempérée
[Termes IGN] habitat forestier
[Termes IGN] sous-boisRésumé : (Auteur) In temperate forests, the highest plant richness is regularly found in the understorey, i.e. shrub, tree regeneration, herbal and moss covers, which provides important food and shelter for other plant and animal species. Here, Light Detection And Ranging (LiDAR) remote sensing was investigated as a surrogate to laborious field surveys to improve understanding of the causal and predictive attributes of understorey. We designed a study in which we used a high-density LiDAR point cloud and applied a thinning algorithm to simulate two lower density point clouds including first and last returns and half of the remaining points (half-thinned data) and only first and last returns (F/L-thinned data). From each dataset, several over- and understorey-related statistical metrics were derived. Each of the three sets of LiDAR metrics was then combined with the forest habitat information to estimate the recorded proportions of shrub, herb and moss coverages. We used three different model procedures including zero-and-one-inflated beta regression (ZOINBR), ordinary least squares with logit-transformed response variables (logistic model) and a machine learning random forest (RF) method. The logistic and ZOINBR model results showed highly significant relationships between LiDAR metrics and habitat types in explaining understorey coverage. The highest coefficients of determination included r2 = 0.80 for shrub cover (estimated by F/L-thinned data and ZOINBR model), r2 = 0.53 for herb cover (estimated by half-thinned data and logistic model) and r2 = 0.48 for moss cover (estimated by half-thinned data and logistic model). RF models returned the best predictive performances (i.e. the lowest root mean square errors). Despite slight differences, no substantial difference was observed amongst the performances achieved by the original, half-thinned and F/L-thinned point clouds. Moreover, the ZOINBR models did not improve predictive performances compared with the logistic model, which suggests that the latter should be preferred due to its greater simplicity and parsimony. Despite the differences between our simulated data and the real-world LiDAR point clouds of different point densities, the results of this study are thought to mostly reflect how LiDAR and forest habitat data can be combined for deriving ecologically relevant information on temperate forest understorey vegetation layers. This, in turn, increases the applicability of prediction results for overarching aims such as forest and wildlife management. Numéro de notice : A2017-906 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1093/forestry/cpw066 Date de publication en ligne : 27/01/2017 En ligne : https://doi.org/10.1093/forestry/cpw066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93195
in Forestry, an international journal of forest research > vol 90 n° 4 (October 2017) . - pp 496 - 514[article]Registration of images to Lidar and GIS data without establishing explicit correspondences / Gabor Barsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)
[article]
Titre : Registration of images to Lidar and GIS data without establishing explicit correspondences Type de document : Article/Communication Auteurs : Gabor Barsai, Auteur ; Alper Yilmaz, Auteur ; Sudhagar Nagarajan, Auteur ; Panu Srestasathiern, Auteur Année de publication : 2017 Article en page(s) : pp 705 - 716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] contour
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image aérienne oblique
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] superposition d'images
[Termes IGN] variable aléatoireRésumé : (auteur) Recovering the camera orientation is a fundamental problem in photogrammetry for precision 3D recovery, orthophoto generation, and image registration. In this paper, we achieve this goal by fusing the image information with information extracted from different modalities, including lidar and GIS. In contrast to other approaches, which require feature correspondences, our approach exploits edges across the modalities without the necessity to explicitly establish correspondences. In the proposed approach, extracted edges from different modalities are not required to have analytical forms. This flexibility is achieved by minimizing a new cost function using a Bayesian approach, which takes the Euclidean distances between the projected edges extracted from the other data source and the edges extracted from the reference image as its random variable. The proposed formulation minimizes the overall distances between the sets of edges iteratively, such that the end product results in the correct camera parameters for the reference image as well as matching features across the modalities. The initial solution can be obtained from GPS/IMU data. The formulation is shown to successfully handle noise and missing observations in edges. Point matching methods may fail for oblique images, especially high oblique images. We eliminate the requirement for exact point-to-point matching. The feasibility of the method is experimented with nadir and oblique images. Numéro de notice : A2017-691 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.10.705 En ligne : https://doi.org/10.14358/PERS.83.10.705 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87858
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 10 (October 2017) . - pp 705 - 716[article]Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
[article]
Titre : Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Marco Heurich, Auteur ; Tiejun Wang, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Allemagne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt alpestre
[Termes IGN] hauteur des arbres
[Termes IGN] lever topographique
[Termes IGN] normalisation
[Termes IGN] reliefRésumé : (Auteur) As an important metric for describing vertical forest structure, the plant area index (PAI) profile is used for many applications including biomass estimation and wildlife habitat assessment. PAI profiles can be estimated with the vertically resolved gap fraction from airborne LiDAR data. Most research utilizes a height normalization algorithm to retrieve local or relative height by assuming the terrain to be flat. However, for many forests this assumption is not valid. In this research, the effect of topographic normalization of airborne LiDAR data on the retrieval of PAI profile was studied in a mountainous forest area in Germany. Results show that, although individual tree height may be retained after topographic normalization, the spatial arrangement of trees is changed. Specifically, topographic normalization vertically condenses and distorts the PAI profile, which consequently alters the distribution pattern of plant area density in space. This effect becomes more evident as the slope increases. Furthermore, topographic normalization may also undermine the complexity (i.e., canopy layer number and entropy) of the PAI profile. The decrease in PAI profile complexity is not solely determined by local topography, but is determined by the interaction between local topography and the spatial distribution of each tree. This research demonstrates that when calculating the PAI profile from airborne LiDAR data, local topography needs to be taken into account. We therefore suggest that for ecological applications, such as vertical forest structure analysis and modeling of biodiversity, topographic normalization should not be applied in non-flat areas when using LiDAR data. Numéro de notice : A2017-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86992
in ISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 77 - 87[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017103 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds / Loïc Landrieu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
[article]
Titre : A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Hugo Raguet, Auteur ; Bruno Vallet , Auteur ; Clément Mallet , Auteur ; Martin Weinmann, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 102 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attribut sémantique
[Termes IGN] données localisées 3D
[Termes IGN] interprétation automatique
[Termes IGN] lissage de données
[Termes IGN] optimisation (mathématiques)
[Termes IGN] régularisation d'image
[Termes IGN] scène
[Termes IGN] semis de pointsRésumé : (Auteur) In this paper, we introduce a mathematical framework for obtaining spatially smooth semantic labelings of 3D point clouds from a pointwise classification. We argue that structured regularization offers a more versatile alternative to the standard graphical model approach. Indeed, our framework allows us to choose between a wide range of fidelity functions and regularizers, influencing the properties of the solution. In particular, we investigate the conditions under which the smoothed labeling remains probabilistic in nature, allowing us to measure the uncertainty associated with each label. Finally, we present efficient algorithms to solve the corresponding optimization problems.
To demonstrate the performance of our approach, we present classification results derived for standard benchmark datasets. We demonstrate that the structured regularization framework offers higher accuracy at a lighter computational cost in comparison to the classic graphical model approach.Numéro de notice : A2017-641 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.010 Date de publication en ligne : 11/09/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86998
in ISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 102 - 118[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017103 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Documents numériques
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