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Terme regroupant photographies et images issues de différents capteurs.
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GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK / Morteza Pourreza in Forests, vol 13 n° 11 (November 2022)
[article]
Titre : GCPs-free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-mounted GNSS RTK Type de document : Article/Communication Auteurs : Morteza Pourreza, Auteur ; Fardin Moradi, Auteur ; Mohammad Khosravi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1905 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] cupressus (genre)
[Termes IGN] diamètre des arbres
[Termes IGN] hauteur de vol
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] Iran
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] point d'appui
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] structure-from-motionRésumé : (auteur) One of the main challenges of using unmanned aerial vehicles (UAVs) in forest data acquisition is the implementation of Ground Control Points (GCPs) as a mandatory step, which is sometimes impossible for inaccessible areas or within canopy closures. This study aimed to test the accuracy of a UAV-mounted GNSS RTK (real-time kinematic) system for calculating tree height and crown height without any GCPs. The study was conducted on a Cupressus arizonica (Greene., Arizona cypress) plantation on the Razi University Campus in Kermanshah, Iran. Arizona cypress is commonly planted as an ornamental tree. As it can tolerate harsh conditions, this species is highly appropriate for afforestation and reforestation projects. A total of 107 trees were subjected to field-measured dendrometric measurements (height and crown diameter). UAV data acquisition was performed at three altitudes of 25, 50, and 100 m using a local network RTK system (NRTK). The crown height model (CHM), derived from a digital surface model (DSM), was used to estimate tree height, and an inverse watershed segmentation (IWS) algorithm was used to estimate crown diameter. The results indicated that the means of tree height obtained from field measurements and UAV estimation were not significantly different, except for the mean values calculated at 100 m flight altitude. Additionally, the means of crown diameter reported from field measurements and UAV estimation at all flight altitudes were not statistically different. Root mean square error (RMSE Numéro de notice : A2022-838 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f13111905 Date de publication en ligne : 12/11/2022 En ligne : https://doi.org/10.3390/f13111905 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102039
in Forests > vol 13 n° 11 (November 2022) . - n° 1905[article]A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation / Fang Gao in Computers & geosciences, vol 168 (November 2022)
[article]
Titre : A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation Type de document : Article/Communication Auteurs : Fang Gao, Auteur ; Yihui Li, Auteur ; Peng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bâtiment
[Termes IGN] filtre de Gauss
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image Jilin
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image satellite
[Termes IGN] lissage de donnéesRésumé : (auteur) The main difficulty of panchromatic-multispectral image fusion is to balance the quality of spatial information and the spectral fidelity. Most of the practical fusion methods determine the optimal parameters based on the spatial and spectral characteristics of all original panchromatic and multispectral bands. However, for built-up and non-built-up areas (like cropland, forest) in one image, there may be large differences in their spatial and spectral characteristics, so their fused results are not optimal respectively with same parameters. To address above issues, this paper presents a high-resolution satellite image fusion method assisted with building segmentation. First, the proposed approach computes the average gradient and Gaussian filtering parameters of built-up and non-built-up areas separately according to the building segmentation results, on the basis of smoothing filter-based intensity modulation (SFIM). Then the intermediate data of two types of areas are computed in parallel and they are composited to obtain the final fused image, weighted by the pixel-wise “building factors” derived from the building segmentation results. Moreover, to better simulate the spatial characteristics of the multispectral image, we perform the “gradient simulation” operation to extract the gradient values in the multispectral image. Experimental results on Jilin-1 satellite images show that the proposed method provides competitive performance in spatial resolution, multispectral fidelity and quantity of information, as compared to the state-of-the-art methods in mainstream commercial software. Numéro de notice : A2022-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105219 Date de publication en ligne : 11/09/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105219 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101657
in Computers & geosciences > vol 168 (November 2022) . - n° 105219[article]A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds / Lina Fang in ISPRS Journal of photogrammetry and remote sensing, vol 193 (November 2022)
[article]
Titre : A joint deep learning network of point clouds and multiple views for roadside object classification from lidar point clouds Type de document : Article/Communication Auteurs : Lina Fang, Auteur ; Zhilong You, Auteur ; Guixi Shen, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 115 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image captée par drone
[Termes IGN] reconnaissance d'objets
[Termes IGN] route
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (auteur) Urban management and survey departments have begun investigating the feasibility of acquiring data from various laser scanning systems for urban infrastructure measurements and assessments. Roadside objects such as cars, trees, traffic poles, pedestrians, bicycles and e-bicycles describe the static and dynamic urban information available for acquisition. Because of the unstructured nature of 3D point clouds, the rich targets in complex road scenes, and the varying scales of roadside objects, finely classifying these roadside objects from various point clouds is a challenging task. In this paper, we integrate two representations of roadside objects, point clouds and multiview images to propose a point-group-view network named PGVNet for classifying roadside objects into cars, trees, traffic poles, and small objects (pedestrians, bicycles and e-bicycles) from generalized point clouds. To utilize the topological information of the point clouds, we propose a graph attention convolution operation called AtEdgeConv to mine the relationship among the local points and to extract local geometric features. In addition, we employ a hierarchical view-group-object architecture to diminish the redundant information between similar views and to obtain salient viewwise global features. To fuse the local geometric features from the point clouds and the global features from multiview images, we stack an attention-guided fusion network in PGVNet. In particular, we quantify and leverage the global features as an attention mask to capture the intrinsic correlation and discriminability of the local geometric features, which contributes to recognizing the different roadside objects with similar shapes. To verify the effectiveness and generalization of our methods, we conduct extensive experiments on six test datasets of different urban scenes, which were captured by different laser scanning systems, including mobile laser scanning (MLS) systems, unmanned aerial vehicle (UAV)-based laser scanning (ULS) systems and backpack laser scanning (BLS) systems. Experimental results, and comparisons with state-of-the-art methods, demonstrate that the PGVNet model is able to effectively identify various cars, trees, traffic poles and small objects from generalized point clouds, and achieves promising performances on roadside object classifications, with an overall accuracy of 95.76%. Our code is released on https://github.com/flidarcode/PGVNet. Numéro de notice : A2022-756 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.08.022 Date de publication en ligne : 22/09/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.08.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101759
in ISPRS Journal of photogrammetry and remote sensing > vol 193 (November 2022) . - pp 115 - 136[article]Machine learning and landslide studies: recent advances and applications / Faraz S. Tehrani in Natural Hazards, vol 114 n° 2 (November 2022)
[article]
Titre : Machine learning and landslide studies: recent advances and applications Type de document : Article/Communication Auteurs : Faraz S. Tehrani, Auteur ; Michele Calvello, Auteur ; Zongqiang Liu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1197 - 1245 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse spatiale
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] effondrement de terrain
[Termes IGN] image Sentinel-MSI
[Termes IGN] réseau neuronal artificiel
[Termes IGN] simulation spatialeRésumé : (auteur) Upon the introduction of machine learning (ML) and its variants, in the form that we know today, to the landslide community, many studies have been carried out to explore the usefulness of ML in landslide research and to look at some classic landslide problems from an ML point of view. ML techniques, including deep learning methods, are becoming popular to model complex landslide problems and are starting to demonstrate promising predictive performance compared to conventional methods. Almost all the studies published in the literature in recent years belong to one of the following three broad categories: landslide detection and mapping, landslide spatial forecasting in the form of susceptibility mapping, and landslide temporal forecasting. In this paper, we present a brief overview of ML techniques, provide a general summary of the landslide studies conducted, in recent years, in the three above-mentioned categories, and make an attempt to critically evaluate the use of ML methods to model landslide processes. The paper also provides suggestions for future use of these powerful data-driven techniques in landslide studies. Numéro de notice : A2022-841 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1007/s11069-022-05423-7 Date de publication en ligne : 20/06/2022 En ligne : https://doi.org/10.1007/s11069-022-05423-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102051
in Natural Hazards > vol 114 n° 2 (November 2022) . - pp 1197 - 1245[article]Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning / Thiên-Anh Nguyen in Remote sensing of environment, vol 281 (November 2022)
[article]
Titre : Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning Type de document : Article/Communication Auteurs : Thiên-Anh Nguyen, Auteur ; Benjamin Kellenberger, Auteur ; Devis Tuia, Auteur Année de publication : 2022 Article en page(s) : n° 113217 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Alpes
[Termes IGN] apprentissage profond
[Termes IGN] canopée
[Termes IGN] carte forestière
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] écotone
[Termes IGN] hauteur des arbres
[Termes IGN] image à très haute résolution
[Termes IGN] image aérienne
[Termes IGN] image RVB
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] SuisseRésumé : (auteur) Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Common methods usually predict a map from remote sensing images without deliberately considering intermediate semantic concepts that are relevant to the final map. This makes the mapping process difficult to interpret, especially when using opaque deep learning models. Moreover, such procedure is entirely agnostic to the definitions of the mapping targets (e.g., forest types depending on variables such as tree height and tree density). Common models can at best learn these rules implicitly from data, which greatly hinders trust in the produced maps. In this work, we aim at building an explainable deep learning model for forest mapping that leverages prior knowledge about forest definitions to provide explanations to its decisions. We propose a model that explicitly quantifies intermediate variables like tree height and tree canopy density involved in the forest definitions, corresponding to those used to create the forest maps for training the model in the first place, and combines them accordingly. We apply our model to mapping forest types using very high resolution aerial imagery and lay particular focus on the treeline ecotone at high altitudes, where forest boundaries are complex and highly dependent on the chosen forest definition. Results show that our rule-informed model is able to quantify intermediate key variables and predict forest maps that reflect forest definitions. Through its interpretable design, it is further able to reveal implicit patterns in the manually-annotated forest labels, which facilitates the analysis of the produced maps and their comparison with other datasets. Numéro de notice : A2022-794 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2022.113217 Date de publication en ligne : 01/09/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101928
in Remote sensing of environment > vol 281 (November 2022) . - n° 113217[article]Measuring visual walkability perception using panoramic street view images, virtual reality, and deep learning / Yunqin Li in Sustainable Cities and Society, vol 86 (November 2022)PermalinkDriving factors of urban sprawl in the Romanian plain. Regional and temporal modelling using logistic regression / Ines Grigorescu in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkModelling and accessing land degradation vulnerability using remote sensing techniques and the analytical hierarchy process approach / Abebe Debele Tolche in Geocarto international, vol 37 n° 24 ([20/10/2022])PermalinkA deep 2D/3D Feature-Level fusion for classification of UAV multispectral imagery in urban areas / Hossein Pourazar in Geocarto international, vol 37 n° 23 ([15/10/2022])PermalinkLand use/land cover mapping from airborne hyperspectral images with machine learning algorithms and contextual information / Ozlem Akar in Geocarto international, vol 37 n° 22 ([10/10/2022])PermalinkApplication of a graph convolutional network with visual and semantic features to classify urban scenes / Yongyang Xu in International journal of geographical information science IJGIS, vol 36 n° 10 (October 2022)PermalinkChallenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images : A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])PermalinkChallenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)PermalinkComparison of layer-stacking and Dempster-Shafer theory-based methods using Sentinel-1 and Sentinel-2 data fusion in urban land cover mapping / Dang Hung Bui in Geo-spatial Information Science, vol 25 n° 3 (October 2022)PermalinkDeep learning-based local climate zone classification using Sentinel-1 SAR and Sentinel-2 multispectral imagery / Lin Zhou in Geo-spatial Information Science, vol 25 n° 3 (October 2022)Permalink