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Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)
[article]
Titre : Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data Type de document : Article/Communication Auteurs : Guo-Hui Yao, Auteur ; Chang-qing Ke, Auteur ; Xiaobing Zhou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 691 - 703 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse multiéchelle
[Termes IGN] bande L
[Termes IGN] classification orientée objet
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données polarimétriques
[Termes IGN] échantillonnage
[Termes IGN] glacier
[Termes IGN] Himalaya
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Landsat-OLI
[Termes IGN] image radar moirée
[Termes IGN] interferométrie différentielle
[Termes IGN] matrice de covariance
[Termes IGN] précision de la classification
[Termes IGN] segmentationRésumé : (auteur) To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. Numéro de notice : A2020-077 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939430 Date de publication en ligne : 25/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2939430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94613
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 691 - 703[article]De l’image optique "multi-stéréo" à la topographie très haute résolution et la cartographie automatique des failles par apprentissage profond / Lionel Matteo (2020)
Titre : De l’image optique "multi-stéréo" à la topographie très haute résolution et la cartographie automatique des failles par apprentissage profond Type de document : Thèse/HDR Auteurs : Lionel Matteo, Auteur Editeur : Nice : Université Côte d'Azur Année de publication : 2020 Importance : 170 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse en vue de l’obtention du grade de docteur de l'Université Côte d'Azur, en Sciences de la Terre et de l’UniversLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] acquisition d'images
[Termes IGN] analyse comparative
[Termes IGN] apprentissage profond
[Termes IGN] Arizona (Etats-Unis)
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données topographiques
[Termes IGN] faille géologique
[Termes IGN] fusion de données multisource
[Termes IGN] image captée par drone
[Termes IGN] image multi sources
[Termes IGN] image Pléiades-HR
[Termes IGN] MicMac
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface
[Termes IGN] Nevada (Etats-Unis)
[Termes IGN] reconstruction 3D
[Termes IGN] semis de points
[Termes IGN] stéréo-orthophotographie
[Termes IGN] traitement de semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Les failles sismogéniques sont la source des séismes. L'étude de leurs propriétés nous informe donc sur les caractéristiques des forts séismes qu'elles peuvent produire. Les failles sont des objets 3D qui forment des réseaux complexes incluant une faille principale et une multitude de failles et fractures secondaires qui "découpent" la roche environnante à la faille principale. Mon objectif dans cette thèse a été de développer des approches pour aider à étudier cette fracturation secondaire intense. Pour identifier, cartographier et mesurer les fractures et les failles dans ces réseaux, j'ai adressé deux défis : -1) Les failles peuvent former des escarpements topographiques très pentus à la surface du sol, créant des "couloirs" ou des canyons étroits et profond où la topographie et donc, la trace des failles, peut être difficile à mesurer en utilisant des méthodologies standard (comme des acquisitions d'images satellites optiques stéréo et tri-stéréo). Pour répondre à ce défi, j'ai utilisé des acquisitions multi-stéréos avec différentes configurations (différents angles de roulis et tangage, différentes dates et modes d'acquisitions). Notre base de données constituée de 37 images Pléiades dans trois sites tectoniques différents dans l'Ouest américain (Valley of Fire, Nevada ; Granite Dells, Arizona ; Bishop Tuff, California) m'a permis de tester différentes configurations d'acquisitions pour calculer la topographie avec trois approches différentes. En utilisant la solution photogrammétrique open-source Micmac (IGN ; Rupnik et al., 2017), j'ai calculé la topographie sous la forme de Modèles Numériques de Surfaces (MNS) : (i) à partir de combinaisons de 2 à 17 images Pléiades, (ii) en fusionnant des MNS calculés individuellement à partir d'acquisitions stéréo et tri-stéréo, évitant alors l'utilisant d'acquisitions multi-dates et (iii) en fusionnant des nuages de points calculés à partir d'acquisitions tri-stéréos en suivant la méthodologie multi-vues développée par Rupnik et al. (2018). J’ai aussi combiné, dans une dernière approche (iv), des acquisitions tri-stéréos avec la méthodologie multi-vues stéréos du CNES/CMLA (CARS) développé par Michel et al. (2020), en combinant des acquisitions tri-stéréos. A partir de ces quatre approches, j'ai calculé plus de 200 MNS et mes résultats suggèrent que deux acquisitions tri-stéréos ou une acquisition stéréo combinée avec une acquisition tri-stéréo avec des angles de roulis opposés permettent de calculer les MNS avec la surface topographique la plus complète et précise. -2) Couramment, les failles sont cartographiées manuellement sur le terrain ou sur des images optiques et des données topographiques en identifiant les traces curvilinéaires qu'elles forment à la surface du sol. Néanmoins, la cartographie manuelle demande beaucoup de temps, ce qui limite notre capacité à produire cartographies et mesures complètes des réseaux de failles. Pour s'affranchir de ce problème, j'ai adopté une approche d'apprentissage profond, couramment appelé un réseau de neurones convolutifs (CNN) - U-Net, pour automatiser l'identification et la cartographie des fractures et des failles dans des images optiques et des données topographiques. Volontairement, le modèle CNN a été entraîné avec une quantité modérée de fractures et failles cartographiées manuellement à basse résolution et dans un seul type d'images optiques (photographies du sol avec des caméras classiques). A partir d'un grand nombre de tests, j'ai sélectionné le meilleur modèle, MRef et démontre sa capacité à prédire des fractures et des failles précisément dans données optiques et topographiques de différents types et différentes résolutions (photographies prises au sol, avec un drone et par satellite). Le modèle MRef montre de bonnes capacités de généralisations faisant alors de ce modèle un bon outil pour cartographie rapidement et précisément des fractures et des failles dans des images optiques et des données topographiques. Note de contenu : Introduction générale
Partie 1 - Reconstruction 3D haute résolution
1. Introduction
1.1 Les données topographiques, une solution pour analyser la surface terrestre
1.2 Le récent développement de satellites à capteur optique
1.3 La reconstruction 3D à partir d’images optiques : la photogrammétrie
1.4 Problématique du sujet
2. Acquisitions de données et sites d’études
2.1 Acquisitions d’images satellitaires
2.2 Données LiDAR aéroportées
2.3 Acquisitions d’images par drone
2.4 Acquisitions d’images par appareil photo suspendu à une perche
2.5 Acquisitions de points d’appui
2.6 Sites d’études
3. Calcul de MNS et estimation de leur performance
3.1 Micmac (IGN)
3.2 CARS (CNES/CMLA)
3.3 Quatre méthodes pour calculer des MNS
3.4 Performances des MNS
4. Résultats
4.1 MNS calculés avec des acquisitions multi-dates
4.2 Fusion de MNS calculés avec des acquisitions mono-dates
4.3 Reconstruction 3D à partir de nuage de points fusionnés
4.4 Analyses des MNS générés avec CARS
4.5 Comparaison des méthodes B, C et D dans la zone de Canyons de Valley of Fire 65
4.6 Utilisation de 1 à 4 GCPs pour calculer un MNS
4.7 Application de la méthode B aux deux autres sites
5. Discussion
5.1 La reconstruction 3D à partir d’acquisitions multi-dates
5.2 L’impact des méthodes B, C et D dans la performance des MNS finaux
5.3 Les erreurs possibles dans le calcul des erreurs du géoréférencement des MNS
5.4 La comparaison des MNS Pléiades calculés à d’autres MNS
6. Conclusions
Partie 2 - Automatic fault mapping in remote optical images and topographic data with deep learning - submitted to JGR: Solid Earth
7. Introduction
8. Image, topographic and fault data
8.1 Fault Sites
8.2 Optical image and topographic data
8.3 Fault ground truth derived from manual mapping
9. Deep learning methodology
9.1 Principles of Deep Learning and Convolutional Neural Networks
9.2 Architecture of the CNN model used in present study
9.3 Training procedure
9.4 Estimating the performance of the models
10. Defining a “reference model” MRef
10.1 Selecting the most appropriate CNN architecture
10.2 Sensitivity of model performance to training data size
10.3 Sensitivity of model performance to “quality” of training data
10.4 Refrence model
11. Detailed evaluation of reference model fault predictions
11.1 Results in sites A and B
11.2 Predictions in unseen data of similar type
11.3 Predictions in unseen data of different type
12. Discussion
12.1 U-net appropriate for fracture and fault detection in optical images
12.2 Interpreting learnt characteristics of faults and fractures
12.3 Conditions for model generalization
12.4 Uncertainties and model robustness
12.5 Recovering fault hierarchy and connectivity
13 Conclusions
Conclusion généraleNuméro de notice : 26555 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Sciences de la Terre et de l'Univers : Côte d'Azur : 2020 Organisme de stage : Géoazur UMR 7329 - Observatoire de la Côte d'Azur nature-HAL : Thèse Date de publication en ligne : 02/06/2021 En ligne : https://tel.archives-ouvertes.fr/tel-03245713/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97965 Image processing applications in object detection and graph matching: from Matlab development to GPU framework / Beibei Cui (2020)
Titre : Image processing applications in object detection and graph matching: from Matlab development to GPU framework Type de document : Thèse/HDR Auteurs : Beibei Cui, Auteur ; Jean-Charles Créput, Directeur de thèse Editeur : Dijon : Université Bourgogne Franche-Comté UBFC Année de publication : 2020 Importance : 137 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Bourgogne Franche-Comté préparée à l'Université de Technologie de Belfort-Montbéliard, InformatiqueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement de graphes
[Termes IGN] détection d'objet
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] graphe planaire
[Termes IGN] Matlab
[Termes IGN] ondelette
[Termes IGN] processeur graphique
[Termes IGN] Ransac (algorithme)
[Termes IGN] reconnaissance de formesIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, flow velocity estimation, identification, registration, and many derived tasks. In this thesis, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute, at a final step, to the design of new and parallel algorithms and their implementation on GPU (Graphics Processing Unit) systems. Graph matching problems can have many declinations, depending on the assumptions of the application at hand. We observed a gap between applications based on local cost objective functions, and those applications with higher-order cost functions, that evaluate similarity between edges of the graphs, or hyperedges when considering hypergraphs. The former class provides convolution-based algorithms already having parallel GPU implementations. Whereas, the latter class puts the emphasis on geometric inter-feature relationships, transforming the correspondence problem to a purely geometric problem stated in a high dimensional space, generally modeled as an integer quadratic programming, for which we did not find GPU implementations available yet.Two complementary approaches were adopted in order to contribute to addressing higher-order geometric graph matching on GPU. Firstly, we study different declinations of feature correspondence problems by the use of the Matlab platform, in order to reuse and provide state-of-the-art solution methods, as well as experimental protocols and input data necessary for a GPU platform with evaluation and comparison tools against existing sequential algorithms, most of the time developed in Matlab framework. Then, the first part of this work concerns three contributions, respectively, to background and frame difference application, to feature extraction problem from images for local correspondences, and to the general graph matching problem, all based on the combination of methods derived from Matlab environment. Secondly, and based on the results of Matlab developments, we propose a new GPU framework written in CUDA C++ specifically dedicated to geometric graph matching but providing new parallel algorithms, with lower computational complexity, as the self-organizing map in the plane, derived parallel clustering algorithms, and distributed local search method. These parallel algorithms are then evaluated and compared to the state-of-the-art methods available for graph matching and following the same experimental protocol. This GPU platform constitutes our final and main proposal to contribute to bridging the gap between GPU development and higher-order graph matching. Note de contenu : 1- Introduction
2- Background
3- Background subtraction and frame difference for multi-object detection
4- Using Marr-wavelets and entropy/response to automatic feature detection
5- Affinity-preserving fixed point APRIP in Matlab framework for graph matching
6- Planar graph matching in GPU
7- Conclusion and future workNuméro de notice : 28328 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : UBFC : 2020 Organisme de stage : CIAD Dijon DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-02902973/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98402
Titre : Des images satellites aux cartes vectorielles Type de document : Thèse/HDR Auteurs : Onur Tasar, Auteur ; Pierre Alliez, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2020 Importance : 151 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée en vue de l'obtention du grade de docteur en Automatique, Traitement du Signal et des Images de l'Université Côte d'AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données massives
[Termes IGN] données matricielles
[Termes IGN] généralisation cartographique
[Termes IGN] géomètrie algorithmique
[Termes IGN] image aérienne
[Termes IGN] image satellite
[Termes IGN] maillage
[Termes IGN] représentation vectorielle
[Termes IGN] segmentation sémantique
[Termes IGN] vectorisationIndex. décimale : THESE Thèses et HDR Résumé : (auteur) With the help of significant technological developments over the years, it has been possible to collect massive amounts of remote sensing data. For example, the constellations of various satellites are able to capture large amounts of remote sensing images with high spatial resolution as well as rich spectral information over the globe. The availability of such huge volume of data has opened the door to numerous applications and raised many challenges. Among these challenges, automatically generating accurate maps has become one of the most interesting and long-standing problems, since it is a crucial process for a wide range of applications in domains such as urban monitoring and management, precise agriculture, autonomous driving, and navigation. This thesis seeks for developing novel approaches to generate vector maps from remote sensing images. To this end, we split the task into two sub-stages. The former stage consists in generating raster maps from remote sensing images by performing pixel-wise classification using advanced deep learning techniques. The latter stage aims at converting raster maps to vector ones by leveraging computational geometry approaches. This thesis addresses the challenges that are commonly encountered within both stages. Although previous research has shown that convolutional neural networks (CNNs)are able to generate excellent maps when training data are representative for test data, their performance significantly drops when there exists a large distribution difference between training and test images. In the first stage of our pipeline, we mainly aim atvercoming limited generalization abilities of CNNs to perform large-scale classification. We also explore a way of leveraging multiple data sets collected at different times with annotations for separate classes to train CNNs that can generate maps for all the classes. In the second part, we propose a method that vectorizes raster maps to integrate them into geographic information systems applications, which completes our processing pipeline. Throughout this thesis, we experiment on a large number of very high resolution satellite and aerial images. Our experiments demonstrate robustness and scalability of the proposed methods. Note de contenu : 1- Introduction
2- Progressively learning to segment new classes
3- City-to-city domain adaptation
4- Multi-source domain adaptation by data standardization
5- Multi-source, multi-target, and life-long domain adaptation
6- Vectorization of buildings via mesh approximation
7- Conclusions and perspectivesNuméro de notice : 28571 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du Signal et des Images : Côte d'Azur : 2020 Organisme de stage : INRIA Sophia Antipolis nature-HAL : Thèse En ligne : https://tel.archives-ouvertes.fr/tel-02989681v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97728 Imaging and diagnostic of sub-wavelength micro-structures, from closed-form algorithms to deep learning / Peipei Ran (2020)
Titre : Imaging and diagnostic of sub-wavelength micro-structures, from closed-form algorithms to deep learning Type de document : Thèse/HDR Auteurs : Peipei Ran, Auteur ; Dominique Lesselier, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2020 Importance : 135 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l’Université Paris-Saclay, Traitement du signal et des imagesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] apprentissage profond
[Termes IGN] chambre anéchoïque
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] diffraction
[Termes IGN] diffusion de Rayleigh
[Termes IGN] hyperfréquence
[Termes IGN] impulsion
[Termes IGN] longueur d'onde
[Termes IGN] micro-onde
[Termes IGN] réseau neuronal récurrent
[Termes IGN] zone d'intérêtIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Electromagnetic probing of a gridlike, finite set of infinitely long circular cylindrical dielectric rods affected by missing ones is investigated from time-harmonic single and multiple frequency data. Sub-wavelength distances between adjacent rods and sub-wavelength rod diameters are assumed throughout the frequency band of operation and this leads to a severe challenge due to need of super-resolution within the present micro-structure, well beyond the Rayleigh criterion. A wealth of solution methods is investigated and comprehensive numerical simulations illustrate pros and cons, completed by processing laboratory-controlled experimental data acquired on a micro-structure prototype in a microwave anechoic chamber. These methods, which differ per a priori information accounted for and consequent versatility, include time-reversal, binary-specialized contrast-source and sparsity-constrained inversions, and convolutional neural networks possibly combined with recurrent ones. Note de contenu : 1- Introduction
2- Modelling of the forward problem
3- Sparsity constrained inversion and contrast source inversion
4- Imaging by convolutional neural networks in frequency domain
5- Imaging by recurrent neural networks in time domain
6- Imaging by convolutional-recurrent neural networks
7- Direct imaging method: time reversal
8- ConclusionNuméro de notice : 28564 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du signal et des images : Université Paris-Saclay : 2020 Organisme de stage : Laboratoire des signaux et systèmes nature-HAL : Thèse En ligne : https://tel.archives-ouvertes.fr/tel-03105752/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97636 PermalinkINS/GNSS integration using recurrent fuzzy wavelet neural networks / Parisa Doostdar in GPS solutions, vol 24 n° 1 (January 2020)PermalinkPermalinkPermalinkInteractions between hierarchical learning and visual system modeling : image classification on small datasets / Thalita Firmo Drumond (2020)PermalinkPermalinkPermalinkPermalinkLearning and geometric approaches for automatic extraction of objects from remote sensing images / Nicolas Girard (2020)PermalinkPermalinkLow-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkPermalinkPermalinkPermalinkA new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods / Yongjiu Feng in International journal of geographical information science IJGIS, vol 34 n° 1 (January 2020)PermalinkNonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou (2020)PermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkPotential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests / Sadeepa Jayathunga in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)PermalinkProbabilistic pose estimation and 3D reconstruction of vehicles from stereo images / Maximilian Alexander Coenen (2020)PermalinkPermalinkPermalinkPermalinkRecherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)PermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles (2020)PermalinkRegional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)PermalinkPermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)PermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)PermalinkPermalinkSpatio-Temporal Prediction of the Epidemic Spread of Dangerous Pathogens Using Machine Learning Methods / Wolfgang B. 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QGIS y sus aplicaciones en la agricultura y la silvicultura / Nicolas Baghdadi (2020)PermalinkVers une occupation du sol France entière par imagerie satellite à très haute résolution / Tristan Postadjian (2020)PermalinkVery high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)PermalinkPermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)PermalinkAn implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkCombining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkData-adaptive spatio-temporal filtering of GRACE data / Paoline Prevost in Geophysical journal international, vol 219 n° 3 (December 2019)PermalinkDeep 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)PermalinkExtracting urban landmarks from geographical datasets using a random forests classifier / Yue Lin in International journal of geographical information science IJGIS, vol 33 n° 12 (December 2019)PermalinkHalf a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkA learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)PermalinkMatching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkNovel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images / Zhi Yong Lv in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkAccurate modelling of canopy traits from seasonal Sentinel-2 imagery based on the vertical distribution of leaf traits / Tawanda W. Gara in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkAn approach for establishing correspondence between OpenStreetMap and reference datasets for land use and land cover mapping / Qi Zhou in Transactions in GIS, Vol 23 n° 6 (November 2019)PermalinkComparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkContext pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkDeep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery / Yuri Shendryk in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)PermalinkA double-strategy-check active learning algorithm for hyperspectral image classification / Ying Cui in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 11 (November 2019)PermalinkMeasuring differential access to facilities between population groups using spatial Lorenz curves and related indices / Gordon A. Cromley in Transactions in GIS, Vol 23 n° 6 (November 2019)PermalinkSig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery / Ruchan Dong in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkTélédétection des habitats insulaires ligériens par drone : Retour d’expérience sur les îles de Mareau-aux-Prés (Loiret) / Hilaire Martin in Revue forestière française, vol 71 n° 6 (2019)PermalinkA temporal phase coherence estimation algorithm and its application on DInSAR pixel selection / Feng Zhao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkUnsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkPotential of Landsat-8 and Sentinel-2A composite for land use land cover analysis / Divyesh Varade in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkResidences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkSegmenting mangrove ecosystems drone images using SLIC superpixels / Edward Zimudzi in Geocarto international, vol 34 n° 14 ([30/10/2019])PermalinkEvolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])PermalinkSea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data / Hua Su in Remote sensing, Vol 11 n° 20 (October-2 2019)PermalinkAccurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network / Yihua Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)PermalinkA CNN-based subpixel level DSM generation approach via single image super-resolution / Yongjun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)PermalinkMapping dead forest cover using a deep convolutional neural network and digital aerial photography / Jean-Daniel Sylvain in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkMulti-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkOptimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkA reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy / Yan Zhou in Transactions in GIS, Vol 23 n° 5 (October 2019)PermalinkSaliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)PermalinkSimulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata / Tingting Xu in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)PermalinkSpatially constrained regionalization with multilayer perceptron / Michael Govorov in Transactions in GIS, Vol 23 n° 5 (October 2019)PermalinkMultitemporal Landsat-MODIS fusion for cropland drought monitoring in El Salvador / Nguyen-Thanh Son in Geocarto international, vol 34 n° 12 ([15/09/2019])PermalinkAddressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkDevelopment and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkEmpirical studies on the visual perception of spatial patterns in choropleth maps / Jochen Schiewe in KN, Journal of Cartography and Geographic Information, vol 69 n° 3 (September 2019)PermalinkA factor model approach for the joint segmentation with between‐series correlation / Xavier Collilieux in Scandinavian Journal of Statistics, vol 46 n° 3 (September 2019)PermalinkLearning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkModelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines / Kassel Hingee in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkOn the application of Monte Carlo singular spectrum analysis to GPS position time series / Seyed Mohsen Khazraei in Journal of geodesy, vol 93 n° 9 (September 2019)PermalinkPlace and sentiment-based life story analysis: From the Spanish republican army to the French resistance / Catherine Dominguès in Revue française des sciences de l'information et de la communication, vol 17 (2019)PermalinkPPD: Pyramid Patch Descriptor via convolutional neural network / Jie Wan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)PermalinkIndividual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])PermalinkLand-cover change in the Wulagai grassland, Inner Mongolia of China between 1986 and 2014 analysed using multi-temporal Landsat images / Temulun Tangud in Geocarto international, vol 34 n° 11 ([15/08/2019])PermalinkEstimating leaf area index and aboveground biomass of grazing pastures using Sentinel-1, Sentinel-2 and Landsat images / Jie Wang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkA generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)PermalinkHigh‐resolution national land use scenarios under a shrinking population in Japan / Haruka Ohashi in Transactions in GIS, vol 23 n° 4 (August 2019)PermalinkImproving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkIncreasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa-Pillai-Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)Permalink“Mapping-with”: The Politics of (Counter-)classification in OpenStreetMap / Clancy Wilmott in Cartographic perspectives, n° 92 (2019)PermalinkPyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkSemantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkEvaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 ([15/07/2019])PermalinkLarge scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain / Convadonga Prendes in iForest, biogeosciences and forestry, vol 12 n° 4 (July 2019)PermalinkA novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery / Tingting Wu in Advances in space research, vol 64 n°1 (1 July 2019)PermalinkStructural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)PermalinkThe AROME-WMED reanalyses of the first special observation period of the Hydrological cycle in the Mediterranean experiment (HyMeX) / Nadia Fourrié in Geoscientific Model Development, vol 12 n° 7 (July 2019)PermalinkA cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])PermalinkComprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])PermalinkDemonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)PermalinkEvaluating metrics derived from Landsat 8 OLI imagery to map crop cover / Rei Sonobe in Geocarto international, vol 34 n° 8 ([15/06/2019])PermalinkHyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])PermalinkAutomatisation du traitement de données "mobile mapping" : extraction d'éléments linéaires et ponctuels / Loïc Elsholz in XYZ, n° 159 (juin 2019)PermalinkCombining low-density LiDAR and satellite images to discriminate species in mixed Mediterranean forest / Angela Blázquez-Casado in Annals of Forest Science, vol 76 n° 2 (June 2019)PermalinkExploitation of deep learning in the automatic detection of cracks on paved roads / Won Mo Jung in Geomatica, vol 73 n° 2 (June 2019)PermalinkA general method for the classification of forest stands using species composition and vertical and horizontal structure / Miquel De Cáceres in Annals of Forest Science, vol 76 n° 2 (June 2019)PermalinkGenetic diversity and structure of Silver fir (Abies alba Mill.) at the south-eastern limit of its distribution range / Maria Teodosiu in Annals of forest research, vol 62 n° 2 (June - December 2019)PermalinkMise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkObject-based random forest modelling of aboveground forest biomass outperforms a pixel-based approach in a heterogeneous and mountain tropical environment / Eduarda M.O. Silveira in International journal of applied Earth observation and geoinformation, vol 78 (June 2019)PermalinkPolarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkA regression model-based method for indoor positioning with compound location fingerprints / Tomofumi Takayama in Geo-spatial Information Science, vol 22 n° 2 (June 2019)PermalinkSemantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)PermalinkPiecewise-planar approximation of large 3D data as graph-structured optimization / Stéphane Guinard in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W5 (May 2019)PermalinkAutomatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network / Jianfeng Huang in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkDetecting and characterizing downed dead wood using terrestrial laser scanning / Tuomas Yrttimaa in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkEstimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery / Yanan Liu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkExploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN / Xinyi Liu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)PermalinkFusion of thermal imagery with point clouds for building façade thermal attribute mapping / Dong Lin in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkMeasuring the influence of map label density on perceived complexity: a user study using eye tracking / Liao Hua in Cartography and Geographic Information Science, vol 46 n° 3 (May 2019)PermalinkPairwise coarse registration of point clouds in urban scenes using voxel-based 4-planes congruent sets / Yusheng Xu in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkVirtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)PermalinkVoxel-based 3D point cloud semantic segmentation: unsupervised geometric and relationship featuring vs deep learning methods / Florent Poux in ISPRS International journal of geo-information, vol 8 n° 5 (May 2019)PermalinkJournées de la recherche 2019 / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)PermalinkMultilane roads extracted from the OpenStreetMap urban road network using random forests / Yongyang Xu in Transactions in GIS, vol 23 n° 2 (April 2019)PermalinkSegmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective / Mohammad D. Hossain in ISPRS Journal of photogrammetry and remote sensing, vol 150 (April 2019)PermalinkDiscrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans / Tanumi Kumar in Geocarto international, vol 34 n° 4 ([15/03/2019])Permalink3D hyperspectral point cloud generation: Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction / Maximilian Brell in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkAn exploratory analysis of usability of Flickr tags for land use/land cover attribution / Yingwei Yan in Geo-spatial Information Science, vol 22 n° 1 (March 2019)PermalinkClimate change and mixed forests: how do altered survival probabilities impact economically desirable species proportions of Norway spruce and European beech? / Carola Paul in Annals of Forest Science, vol 76 n° 1 (March 2019)PermalinkConditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkDuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkForest degradation and biomass loss along the Chocó region of Colombia / Victoria Meyer in Carbon Balance and Management, vol 14 (March 2019)PermalinkHyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkInferring user tasks in pedestrian navigation from eye movement data in real-world environments / Hua Liao in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkLand cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms / Dimitri Bulatov in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkModeling and visualizing semantic and spatio-temporal evolution of topics in interpersonal communication on Twitter / Caglar Koylu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkA natural language processing and geospatial clustering framework for harvesting local place names from geotagged housing advertisements / Yingjie Hu in International journal of geographical information science IJGIS, Vol 33 n° 3-4 (March - April 2019)PermalinkSemantic understanding of scenes through the ADE20K dataset / Bolei Zhou in International journal of computer vision, vol 127 n° 3 (March 2019)PermalinkStem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)PermalinkTree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)PermalinkUsing LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)PermalinkA simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)PermalinkComplete 3D scene parsing from an RGBD image / Chuhang Zou in International journal of computer vision, vol 127 n° 2 (February 2019)PermalinkGeoTxt: A scalable geoparsing system for unstructured text geolocation / Morteza Karimzadeh in Transactions in GIS, vol 23 n° 1 (February 2019)PermalinkImproving LiDAR classification accuracy by contextual label smoothing in post-processing / Nan Li in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)PermalinkLearning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkA local projection-based approach to individual tree detection and 3-D crown delineation in multistoried coniferous forests using high-density airborne LiDAR data / Aravind Harikumar in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)PermalinkSynergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])PermalinkTree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])PermalinkPermalinkAdvanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure / Maged Marghany (2019)PermalinkAilanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkAnalyse de données d’OpenStreetMap en vue de discriminer l’usage du sol lié au bâti / Jocelyn Le Maître (2019)PermalinkAnalyse d’images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées / Khouloud Dahmane (2019)PermalinkBayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)PermalinkBridging the gap: toward a French MS-NFI for territories / Jean-Pierre Renaud (2019)PermalinkClassification du type et de la concentration de la banquise, à partir d’images Sentinel-1 SAR, grâce à des réseaux de neurones convolutifs / Hugo Boulze (2019)PermalinkPermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. Vargas-Muñoz in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkDataPink, l'IA au service de l'information géographique / Anonyme in Géomatique expert, n° 126 (janvier - février 2019)PermalinkDetecting arbitrarily shaped clusters in origin-destination flows using ant colony optimization / Si Song in International journal of geographical information science IJGIS, Vol 33 n° 1-2 (January - February 2019)PermalinkDétection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)PermalinkPermalinkEnhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine / Hossam Talaat Elshambaky in Journal of applied geodesy, vol 13 n° 1 (January 2019)PermalinkEnrichissement d'orthophotographie par des données OpenStreetMap pour l'apprentissage machine / Gauthier Fillières-Riveau (2019)PermalinkPermalinkPermalinkEstimation de profondeur à partir d'images monoculaires par apprentissage profond / Michel Moukari (2019)PermalinkEvaluating the capability of the Sentinel 2 data for soil organic carbon prediction in croplands / Fabio Castaldi in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkPermalinkPermalinkPermalinkPermalinkGeographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)PermalinkPermalinkA growth-model-driven technique for tree stem diameter estimation by using airborne LiDAR data / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkIntegration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)PermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkLU-Net, An efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-Net / Pierre Biasutti (2019)PermalinkMachine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)PermalinkPermalinkManuel de géographie quantitative / Thierry Feuillet (2019)PermalinkMeasuring stem diameters with TLS in boreal forests by complementary fitting procedure / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkMéthodes d'apprentissage statistique pour la détection de la signalisation routière à partir de véhicules traceurs / Yann Méneroux (2019)PermalinkMéthodes d'exploitation de données historiques pour la production de cartes d'occupation des sols à partir d'images de télédétection et en absence de données de référence de la période à cartographier / Benjamin Tardy (2019)PermalinkA multi-faceted CNN architecture for automatic classification of mobile LiDAR data and an algorithm to reproduce point cloud samples for enhanced training / Bhavesh Kumar in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkMultimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)PermalinkPermalinkPermalinkPermalinkPotentialités de l’imagerie couleur embarquée pour la détection et la cartographie des maladies fongiques de la vigne / Florent Abdelghafour (2019)PermalinkPermalinkPermalinkPermalinkSegmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)Permalink