Descripteur
Termes IGN > informatique > intelligence artificielle > apprentissage automatique > apprentissage dirigé
apprentissage dirigéSynonyme(s)apprentissage superviséVoir aussi |
Documents disponibles dans cette catégorie (114)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Analyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2 / François Messner in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 2018)
[article]
Titre : Analyse de l'incertitude et de la précision thématique de classifications GEOBIA d'une image WorldView-2 Type de document : Article/Communication Auteurs : François Messner, Auteur ; Jeannine Corbonnois, Auteur ; Fanny Stella Tchitouo Ntenzou, Auteur Année de publication : 2018 Article en page(s) : pp 19 - 37 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] arbre de décision
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] distance de Kullback-Leibler
[Termes IGN] ensachage
[Termes IGN] entropie
[Termes IGN] image Worldview
[Termes IGN] modèle orienté objet
[Termes IGN] précision de la classification
[Termes IGN] Sarthe (72)Résumé : (Auteur) L'évaluation de la précision des cartes thématiques produites par télédétection est une finalité de tout processus de classification modélisant le paysage. Reposant traditionnellement sur la matrice de confusion, elle peut être complétée par des méthodes alternatives plus à même de prendre en compte le biais relatif à la sélection des échantillons d'apprentissage, ainsi que par l'emploi d'approches représentant spatialement l'incertitude inhérente aux classifications. Une telle démarche est adoptée dans cet article, en évaluant la précision à l'aide des estimateurs du Maximum de Probabilité a Posteriori, puis en déterminant, pour chaque unité de carte, des mesures d'incertitude : l'entropie a quadratique, la divergence de Kullback-Leibler et un indice de concordance qualitatif. Ces traitements sont analysés et comparés selon 3 classifieurs, Random Forest, C5.0 et l'Analyse Discriminante Linéaire et selon 4 stratégies de classification : classifieurs seuls, classifieurs avec procédure de bagging, classifieurs avec procédure d'apprentissage actifs et classifieurs avec procédure d'apprentissage actif et de bagging. Les résultats obtenus soulignent la complémentarité des estimateurs de précision pour mettre en évidence un biais dans l'évaluation de la précision ou dans la détermination des probabilités a posteriori, et justifie la prise en considération des indices d'incertitude comme source d'informations sur la distribution spatiale des erreurs de cartographie. Numéro de notice : A2018-092 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2018.310 Date de publication en ligne : 19/04/2018 En ligne : https://doi.org/10.52638/rfpt.2018.310 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89502
in Revue Française de Photogrammétrie et de Télédétection > n° 216 (février 2018) . - pp 19 - 37[article]Semantic enrichment of octree structured point clouds for multi‐story 3D pathfinding / Florian W. Fichtner in Transactions in GIS, vol 22 n° 1 (February 2018)
[article]
Titre : Semantic enrichment of octree structured point clouds for multi‐story 3D pathfinding Type de document : Article/Communication Auteurs : Florian W. Fichtner, Auteur ; Abdoulaye A. Diakité, Auteur ; Sisi Zlatanova, Auteur ; Robert Voûte, Auteur Année de publication : 2018 Article en page(s) : pp 233 - 248 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] calcul d'itinéraire
[Termes IGN] espace intérieur vide
[Termes IGN] information sémantique
[Termes IGN] octree
[Termes IGN] positionnement en intérieur
[Termes IGN] semis de pointsRésumé : (auteur) 3D indoor navigation in multi‐story buildings and under changing environments is still difficult to perform. 3D models of buildings are commonly not available or outdated. 3D point clouds turned out to be a very practical way to capture 3D interior spaces and provide a notion of an empty space. Therefore, pathfinding in point clouds is rapidly emerging. However, processing of raw point clouds can be very expensive, as these are semantically poor and unstructured data. In this article we present an innovative octree‐based approach for processing of 3D indoor point clouds for the purpose of multi‐story pathfinding. We semantically identify the construction elements, which are of importance for the indoor navigation of humans (i.e., floors, walls, stairs, and obstacles), and use these to delineate the available navigable space. To illustrate the usability of this approach, we applied it to real‐world data sets and computed paths considering user constraints. The structuring of the point cloud into an octree approximation improves the point cloud processing and provides a structure for the empty space of the point cloud. It is also helpful to compute paths sufficiently accurate in their consideration of the spatial complexity. The entire process is automatic and able to deal with a large number of multi‐story indoor environments. Numéro de notice : A2018-067 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12308 En ligne : https://doi.org/10.1111/tgis.12308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89423
in Transactions in GIS > vol 22 n° 1 (February 2018) . - pp 233 - 248[article]Active learning-based optimized training library generation for object-oriented image classification / Rajeswari Balasubramaniam in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)
[article]
Titre : Active learning-based optimized training library generation for object-oriented image classification Type de document : Article/Communication Auteurs : Rajeswari Balasubramaniam, Auteur ; Srivalsan Namboodiri, Auteur ; Rama Rao Nidamanuri, Auteur ; Rama Krishna Sai Subrahmanyam Gorthi, Auteur Année de publication : 2018 Article en page(s) : pp 575 - 585 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image aérienne
[Termes IGN] image multibandeRésumé : (Auteur) In this paper, we introduce an active learning (AL)-based object training library generation for a multiclassifier object-oriented image analysis (OOIA) system. While several AL approaches do exist for pixel-based training library generation and for hyperspectral image classification, there is no standard training library generation strategy for OOIA of very high spatial resolution images. Given a sufficient number of training samples, supervised classification is the method of choice for image classification. However, this strategy becomes computationally expensive with the increase in the number of classes or the number of images to be classified. The above-mentioned issue is solved in this proposed method, where an optimized training library of objects (superpixels) is generated based on a batch mode AL approach. A softmax classifier is used as a detector in this method, which helps in determining the right samples to be chosen for library updation. To this end, we construct a multiclassifier system with max-voting decision to classify an image at pixel level. This algorithm was applied on three different very high-resolution airborne data sets, each with varying complexity in terms of variations in geographical context, sensors, illumination, and view angles. Our method has empirically outperformed the traditional OOIA by producing equivalent accuracy with a training library that is orders of magnitude smaller. In addition, the most distinctive ability of the algorithm is experienced in the most heterogeneous data set, where its performance in terms of accuracy is around twice the performance of the traditional method in the same situation. The generality of this classification strategy is proved through its performance on multispectral images and for cross-domain application. Finally, the robustness of this method is identified by comparing its performance with an alternative AL approach-self-learning-based semisupervised SVM. The capability of the proposed method to handle highly heterogeneous data is identified as the primary reason for its robustness. Numéro de notice : A2018-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2751568 Date de publication en ligne : 29/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2751568 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89847
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 575 - 585[article]Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
[article]
Titre : Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China Type de document : Article/Communication Auteurs : Ran Jing, Auteur ; Zhaoning Gong, Auteur ; Wenji Zhao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 122 - 134 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] arbre de décision
[Termes IGN] biomasse
[Termes IGN] croissance végétale
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] indice de végétation
[Termes IGN] lac
[Termes IGN] macrophyte
[Termes IGN] modèle de régression
[Termes IGN] orthoimage
[Termes IGN] Pékin (Chine)
[Termes IGN] régression linéaire
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] zone humideRésumé : (Auteur) Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated. Numéro de notice : A2017-732 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88431
in ISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 122 - 134[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017122 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017123 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Complex-valued convolutional neural network and its application in polarimetric SAR image classification / Zhimian Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)
[article]
Titre : Complex-valued convolutional neural network and its application in polarimetric SAR image classification Type de document : Article/Communication Auteurs : Zhimian Zhang, Auteur ; Haipeng Wang, Auteur ; Feng Xu, Auteur ; Ya-Qiu Jin, Auteur Année de publication : 2017 Article en page(s) : pp 7177 - 7188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage dirigé
[Termes IGN] données polarimétriques
[Termes IGN] image radar moirée
[Termes IGN] polarimétrie radar
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Following the great success of deep convolutional neural networks (CNNs) in computer vision, this paper proposes a complex-valued CNN (CV-CNN) specifically for synthetic aperture radar (SAR) image interpretation. It utilizes both amplitude and phase information of complex SAR imagery. All elements of CNN including input-output layer, convolution layer, activation function, and pooling layer are extended to the complex domain. Moreover, a complex backpropagation algorithm based on stochastic gradient descent is derived for CV-CNN training. The proposed CV-CNN is then tested on the typical polarimetric SAR image classification task which classifies each pixel into known terrain types via supervised training. Experiments with the benchmark data sets of Flevoland and Oberpfaffenhofen show that the classification error can be further reduced if employing CV-CNN instead of conventional real-valued CNN with the same degrees of freedom. The performance of CV-CNN is comparable to that of existing state-of-the-art methods in terms of overall classification accuracy. Numéro de notice : A2017-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2743222 En ligne : https://doi.org/10.1109/TGRS.2017.2743222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88810
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 7177 - 7188[article]Global, dense multiscale reconstruction for a billion points / Benjamin Ummenhofer in International journal of computer vision, vol 125 n° 1-3 (December 2017)PermalinkMultilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification / Yanqiao Chen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkLearning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkA morphologically preserved multi-resolution TIN surface modeling and visualization method for virtual globes / Xianwei Zheng in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkVariable-scale maps in real-time generalisation using a quadtree data structure and space deforming algorithms / Pia Bereuter in International journal of cartography, vol 3 n° 1 (June 2017)PermalinkDimensionality reduction and classification of hyperspectral images using ensemble discriminative local metric learning / Yanni Dong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkClassifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkDictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkPredicting the encoding of secondary diagnoses. An experience based on decision trees / Ghazar Chahbandarian in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 22 n° 2 (mars - avril 2017)PermalinkStatistical Relational Learning of Grammar Rules for 3D Building Reconstruction / Youness Dehbi in Transactions in GIS, vol 21 n° 1 (February 2017)PermalinkPermalinkPermalinkEtude et méthodes d'intégration et d'interaction de données 3D complexes type "nuages de points" vers un web SIG / Victor Lambert (2017)PermalinkPermalinkThe use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery / Ismail Colkesen in Geocarto international, vol 32 n° 1 (January 2017)PermalinkHow many samples are needed? An investigation of binary logistic regression for selective omission in a road network / Qi Zhou in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)PermalinkNoise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkEfficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkGrid pattern recognition in road networks using the C4.5 algorithm / Jing Tian in Cartography and Geographic Information Science, Vol 43 n° 3 (June 2016)Permalink