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Les journées de la Recherche IGN 2021 / Anonyme in Géomatique expert, n° 135 (septembre 2021)
[article]
Titre : Les journées de la Recherche IGN 2021 Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2021 Conférence : Journées Recherche de l'IGN 2021, 30es Journées 25/05/2021 28/05/2021 en ligne France vidéos des journées Article en page(s) : pp 36 - 47 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Information géographique
[Termes IGN] appariement d'images
[Termes IGN] BD Topo
[Termes IGN] chronométrie
[Termes IGN] Copernicus (programme européen)
[Termes IGN] correction radiométrique
[Termes IGN] déformation de surface
[Termes IGN] données GRACE
[Termes IGN] éclairement lumineux
[Termes IGN] extraction de données
[Termes IGN] fonte des glaces
[Termes IGN] horloge atomique
[Termes IGN] ilot thermique urbain
[Termes IGN] image aérienne
[Termes IGN] intelligence artificielle
[Termes IGN] MicMac
[Termes IGN] modèle numérique de surface
[Termes IGN] recherche scientifique
[Termes IGN] série temporelle
[Termes IGN] surveillance sanitaire
[Termes IGN] visualisation de donnéesRésumé : (Auteur) L’édition 2021 des Journées de la Recherche à l’IGN s’est déroulée, cette année encore, « en ligne ». C’était, malgré tout, l’occasion de marquer la trentième édition de l’événement. Numéro de notice : A2021-661 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98481
in Géomatique expert > n° 135 (septembre 2021) . - pp 36 - 47[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002273 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
[article]
Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Brésil
[Termes IGN] carte de la végétation
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité de la végétation
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 Date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175
in ISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021091 SL Revue Centre de documentation Revues en salle Disponible 081-2021093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Two hidden layer neural network-based rotation forest ensemble for hyperspectral image classification / Laxmi Narayana Eeti in Geocarto international, vol 36 n° 16 ([01/09/2021])
[article]
Titre : Two hidden layer neural network-based rotation forest ensemble for hyperspectral image classification Type de document : Article/Communication Auteurs : Laxmi Narayana Eeti, Auteur ; Krishna Mohan Buddhiraju, Auteur Année de publication : 2021 Article en page(s) : pp 1820 - 1837 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre de décision
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] ensachage
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] Perceptron multicouche
[Termes IGN] précision de la classification
[Termes IGN] réseau neuronal profond
[Termes IGN] Rotation Forest classificationRésumé : (auteur) Decision tree-based Rotation Forest could generate satisfactory but lower classification accuracy for a given training sample set and image data, owing to the inherent disadvantages in decision trees, namely myopic, replication and fragmentation problem. To improve performance of Rotation Forest technique, we propose to utilize two-hidden-layered-feedforward neural network as base classifier instead of decision tree. We examine the classification performance of proposed model under two situations, namely when free network parameters are maintained the same across all ensemble components and otherwise. The proposed model, where each component is initialized with different pair of initial weights and bias, performs better than decision tree-based Rotation Forest on three different Hyperspectral sensor datasets – AVIRIS, ROSIS and Hyperion. Improvements in classification accuracy are above 2% and up to 3% depending upon dataset. Also, the proposed model achieves improvement in accuracy over Random Forest in the range 4.2–8.8%. Numéro de notice : A2021-581 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1678680 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1080/10106049.2019.1678680 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98193
in Geocarto international > vol 36 n° 16 [01/09/2021] . - pp 1820 - 1837[article]Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] / Hamza Ben Addou in Géomatique expert, n° 135 (septembre 2021)
[article]
Titre : Utilisation de l'apprentissage profond dans la modélisation 3D urbaine [Partie 1] Type de document : Article/Communication Auteurs : Hamza Ben Addou, Auteur Année de publication : 2021 Article en page(s) : pp 11 - 20 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] emprise au sol
[Termes IGN] fusion de données multisource
[Termes IGN] image aérienne
[Termes IGN] information sémantique
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) Partie 1 : Mise en place d’un processus de détection automatique des emprises de bâtiments par apprentissage profond Numéro de notice : A2021-660 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/09/2021 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98414
in Géomatique expert > n° 135 (septembre 2021) . - pp 11 - 20[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité IFN-001-P002273 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN) / Zhenbang Hao in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Automated tree-crown and height detection in a young forest plantation using mask region-based convolutional neural network (Mask R-CNN) Type de document : Article/Communication Auteurs : Zhenbang Hao, Auteur ; Lili Lin, Auteur ; Christopher J. Post, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 112 - 123 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Abies (genre)
[Termes IGN] Abies numidica
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] hauteur des arbres
[Termes IGN] houppier
[Termes IGN] image captée par drone
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] plantation forestièreRésumé : (auteur) Tree-crown and height are primary tree measurements in forest inventory. Convolutional neural networks (CNNs) are a class of neural networks, which can be used in forest inventory; however, no prior studies have developed a CNN model to detect tree crown and height simultaneously. This study is the first-of-its-kind that explored training a mask region-based convolutional neural network (Mask R-CNN) for automatically and concurrently detecting discontinuous tree crown and height of Chinese fir (Cunninghamia lanceolata (Lamb) Hook) in a plantation. A DJI Phantom4-Multispectral Unmanned Aerial Vehicle (UAV) was used to obtain high-resolution images of the study site, Shunchang County, China. Tree crown and height of Chinese fir was manually delineated and derived from this UAV imagery. A portion of the ground-truthed tree height values were used as a test set, and the remaining measurements were used as the model training data. Six different band combinations and derivations of the UAV imagery were used to detect tree crown and height, respectively (Multi band-DSM, RGB-DSM, NDVI-DSM, Multi band-CHM, RGB-CHM, and NDVI-CHM combination). The Mask R-CNN model with the NDVI-CHM combination achieved superior performance. The accuracy of Chinese fir’s individual tree-crown detection was considerable (F1 score = 84.68%), the Intersection over Union (IoU) of tree crown delineation was 91.27%, and tree height estimates were highly correlated with the height from UAV imagery (R2 = 0.97, RMSE = 0.11 m, rRMSE = 4.35%) and field measurement (R2 = 0.87, RMSE = 0.24 m, rRMSE = 9.67%). Results demonstrate that the input image with an CHM achieves higher accuracy of tree crown delineation and tree height assessment compared to an image with a DSM. The accuracy and efficiency of Mask R-CNN has a great potential to assist the application of remote sensing in forests. Numéro de notice : A2021-563 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2021.06.003 Date de publication en ligne : 18/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98128
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 112 - 123[article]Réservation
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Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)PermalinkPrecise extraction of citrus fruit trees from a Digital Surface Model using a unified strategy: detection, delineation, and clustering / Ali Ozgun Ok in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)PermalinkVehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkCNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkDevelopment and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkGuided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkLeveraging photogrammetric mesh models for aerial-ground feature point matching toward integrated 3D reconstruction / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)PermalinkPredicting biomass dynamics at the national extent from digital aerial photogrammetry / Bronwyn Price in International journal of applied Earth observation and geoinformation, vol 90 (August 2020)PermalinkEvaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkMapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery / Kasper Johansen in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)PermalinkAccuracy assessment of real-time kinematics (RTK) measurements on unmanned aerial vehicles (UAV) for direct geo-referencing / Desta Ekaso in Geo-spatial Information Science, vol 23 n° 2 (June 2020)Permalink