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Fine-grained object recognition and zero-shot learning in remote sensing imagery / Gencer Sumbul in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)
[article]
Titre : Fine-grained object recognition and zero-shot learning in remote sensing imagery Type de document : Article/Communication Auteurs : Gencer Sumbul, Auteur ; Ramazan Gokberk Cinbis, Auteur ; Selim Aksoy, Auteur Année de publication : 2018 Article en page(s) : pp 770 - 779 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] arbre urbain
[Termes IGN] image numérique
[Termes IGN] inférence
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) Fine-grained object recognition that aims to identify the type of an object among a large number of subcategories is an emerging application with the increasing resolution that exposes new details in image data. Traditional fully supervised algorithms fail to handle this problem where there is low between-class variance and high within-class variance for the classes of interest with small sample sizes. We study an even more extreme scenario named zero-shot learning (ZSL) in which no training example exists for some of the classes. ZSL aims to build a recognition model for new unseen categories by relating them to seen classes that were previously learned. We establish this relation by learning a compatibility function between image features extracted via a convolutional neural network and auxiliary information that describes the semantics of the classes of interest by using training samples from the seen classes. Then, we show how knowledge transfer can be performed for the unseen classes by maximizing this function during inference. We introduce a new data set that contains 40 different types of street trees in 1-ft spatial resolution aerial data, and evaluate the performance of this model with manually annotated attributes, a natural language model, and a scientific taxonomy as auxiliary information. The experiments show that the proposed model achieves 14.3% recognition accuracy for the classes with no training examples, which is significantly better than a random guess accuracy of 6.3% for 16 test classes, and three other ZSL algorithms. Numéro de notice : A2018-190 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2754648 Date de publication en ligne : 18/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2754648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89855
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 770 - 779[article]Large-scale remote sensing image retrieval by deep hashing neural networks / Yansheng Li in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)
[article]
Titre : Large-scale remote sensing image retrieval by deep hashing neural networks Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Yongjun Zhang, Auteur ; Xin Huang, Auteur ; Hu Zhu, Auteur ; Jiayi Ma, Auteur Année de publication : 2018 Article en page(s) : pp 950 - 965 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] données d'entrainement (apprentissage automatique)Résumé : (Auteur) As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions. Numéro de notice : A2018-192 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2756911 Date de publication en ligne : 13/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2756911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89857
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 950 - 965[article]Multisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)
[article]
Titre : Multisource remote sensing data classification based on convolutional neural network Type de document : Article/Communication Auteurs : Xiaodong Xu, Auteur ; Wei Li, Auteur ; Qiong Ran, Auteur ; Qian Du, Auteur ; Lianru Gao, Auteur ; Bing Zhang, Auteur Année de publication : 2018 Article en page(s) : pp 937 - 949 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal convolutifRésumé : (Auteur) As a list of remotely sensed data sources is available, how to efficiently exploit useful information from multisource data for better Earth observation becomes an interesting but challenging problem. In this paper, the classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN). More specific, a two-tunnel CNN framework is first developed to extract spectral-spatial features from HSI; besides, the CNN with cascade block is designed for feature extraction from LiDAR or high-resolution visual image. In the feature fusion stage, the spatial and spectral features of HSI are first integrated in a dual-tunnel branch, and then combined with other data features extracted from a cascade network. Experimental results based on several multisource data demonstrate the proposed two-branch CNN that can achieve more excellent classification performance than some existing methods. Numéro de notice : A2018-191 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2756851 Date de publication en ligne : 16/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2756851 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89856
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 937 - 949[article]
Titre : Advances in airborne Lidar systems and data processing Type de document : Monographie Auteurs : Jie Shan, Éditeur scientifique ; Juha Hyyppä, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2018 Importance : 493 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03842-673-8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] détection d'arbres
[Termes IGN] données lidar
[Termes IGN] enregistrement de données
[Termes IGN] hauteur des arbres
[Termes IGN] image multibande
[Termes IGN] modèle numérique de terrain
[Termes IGN] photon
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] télédétection par lidarRésumé : (éditeur) This book collects the papers in the special issue "Airborne Laser Scanning" in Remote Sensing (Nov. 2016) and several other selected papers published in the same journal in the past few years. Our intention is to reflect recent technological developments and innovative techniques in this field. The book consists of 23 papers in six subject areas: 1) Single photon and Geiger-mode Lidar, 2) Multispectral lidar, 3) Waveform lidar, 4) Registration of point clouds, 5) Trees and terrain, and 6) Building extraction. The book is a valuable resource for scientists, engineers, developers, instructors, and graduate students interested in lidar systems and data processing. Note de contenu : 1- Single photon and Geiger-mode Lidar
2- Multispectral Lidar
3- Waveform Lidar
4- Registration of Point Clouds
5- Trees and Terrain
6- Building ExtractionNuméro de notice : 25932 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie En ligne : https://doi.org/10.3390/books978-3-03842-674-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96235
Titre : Apprentissage de modalités auxiliaires pour la localisation basée vision Type de document : Article/Communication Auteurs : Nathan Piasco , Auteur ; Désiré Sidibé, Auteur ; Valérie Gouet-Brunet , Auteur ; Cédric Demonceaux, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2018 Projets : PLaTINUM / Gouet-Brunet, Valérie Conférence : RFIAP 2018, Reconnaissance des Formes, Image, Apprentissage et Perception 01/06/2018 01/06/2018 Champs-sur-Marne France Open Access Proceedings Importance : 8 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] localisation basée vision
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) Dans cet article, nous présentons une nouvelle méthode d’apprentissage à partir de modalités auxiliaires pour améliorer un système de localisation basée vision. Afin de bénéficier des informations de modalités auxiliaires disponibles pendant l’apprentissage, nous entraînons un réseau convolutif à recréer l’apparence de ces modalités annexes. Nous validons notre approche en l’appliquant à un problème de description d’images pour la localisation. Les résultats obtenus montrent que notre système est capable d’améliorer un descripteur d’images en apprenant correctement l’apparence d’une modalité annexe. Comparé à l’état de l’art, le réseau présenté permet d’obtenir des résultats de localisation comparables, tout en étant plus compacte et plus simple à entraîner. Numéro de notice : C2018-006 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésNat DOI : sans Date de publication en ligne : 28/06/2018 En ligne : https://rfiap2018.ign.fr/programmeCFPT Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90335 Documents numériques
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Mas in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)PermalinkCSTST 2008, the 5th International conference on soft computing as transdisciplinary science and technology, October 28th - October 31st 2008, University of Cergy-Pontoise, France / Richard Chbeir (2008)PermalinkA supervised artificial immune classifier for remote-sensing imagery / Y. Zhong in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkVisibility prediction based on artificial neural networks used in automatic network design / M. Saadatseresht in Photogrammetric record, vol 22 n° 120 (December 2007 - February 2008)PermalinkArtificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland / S. Chattopadhyay in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkBrainy positioning: processing GPS data with neural networks / Rodrigo Figueiredo Leandro in GPS world, vol 18 n° 9 (September 2007)PermalinkMapping of environmental data using kernel-based methods / Mikhail Kanevski in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)PermalinkArtificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. 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Qi in International journal of geographical information science IJGIS, vol 17 n° 8 (december 2003)PermalinkA neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery / E. Binaghi in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkSimulation of development alternatives using neural networks, cellular automata, and GIS for urban planning / A.G. Yeh in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkWater quality retrievals from combined Landsat TM data and ERS-2 SAR data in the Gulf of Finland / Y. Zhang in IEEE Transactions on geoscience and remote sensing, vol 41 n° 3 (March 2003)PermalinkLe boosting : une méthode de classification non paramétrique / Michel Arnaud in Revue internationale de géomatique, vol 12 n° 4 (décembre 2002 – février 2003)PermalinkCalibration of stochastic cellular automata: the application to rural-urban land conversions / F. Wu in International journal of geographical information science IJGIS, vol 16 n° 8 (december 2002)PermalinkNeural-network-based cellular automata for simulating multiple land use changes using GIS / X. Li in International journal of geographical information science IJGIS, vol 16 n° 4 (june 2002)PermalinkArtificial neural networks as a method of spatial interpolation for digital elevation models / D.A. Merwin in Cartography and Geographic Information Science, vol 29 n° 2 (April 2002)PermalinkECAI 2002, 15th European Conference on Artificial Intelligence, July 21-26, Lyon, France / Frank Van Harmelen (2002)PermalinkGénéralisation et représentation multiple / Anne Ruas (2002)PermalinkRetrieval of sea water optically active parameters from hyperspectral data by means of generalized radial basis function neural networks / P. Cipollini in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)PermalinkArtificial neural networks as a tool for spatial interpolation / J.P. Rigol in International journal of geographical information science IJGIS, vol 15 n° 4 (june 2001)PermalinkA neural network image interpretation system to extract rural and urban land use and land cover information from remote sensor data / J.R. Jensen in Geocarto international, vol 16 n° 1 (March - May 2001)PermalinkGeoComputational modelling / Manfred M. Fischer (2001)PermalinkSpatial prediction of fire ignition probabilities: comparing logistic regression and neural networks / M.J. Perestrello De Vasconcelos in Photogrammetric Engineering & Remote Sensing, PERS, vol 67 n° 1 (January 2001)PermalinkAdvanced polarimetric SAR data classification for cartographic information extraction / Manfred F. Buchroithner (31/05/1999)PermalinkBeschreibung von Deformationsprozessen durch Volterra- und Fuzzy-Modelle sowie neuronale Netze / K. Heine (1999)PermalinkConférence d'apprentissage 99, actes de CAP'99, Ecole Polytechnique, Palaiseau, 15-18 juin 1999 / Michèle Sebag (1999)PermalinkThe ASTER polar cloud mask / A.M. Logar in IEEE Transactions on geoscience and remote sensing, vol 36 n° 4 (July 1998)PermalinkUsing genetic learning neural networks for spatial decision making in GIS / J. Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 62 n° 11 (november 1996)PermalinkProceedings of the second workshop Application of artificial intelligence techniques in seismology and engineering seismology / M. Garcia-Fernandez (1996)PermalinkTélédétection aérospatiale / Jules Wilmet (1996)PermalinkArtificial intelligence / Stuart J. Russell (1995)PermalinkCours d'informatique du professeur Bouillé / François Bouillé (1995)PermalinkProceedings of the workshop Dynamical systems and artificial intelligence applied to data banks in geophysics / J. Bonnin (1995)PermalinkRemote sensing in action, RSS95, Proceedings of the 21th annual conference of the Remote Sensing Society RSS, Southampton, 11-14 septembre 1995 / P.J. Curran (1995)PermalinkSystème de cognition artificielle : Application au problème géographique général / Ching-Han Chen (1995)PermalinkAmélioration de la détection de contours en imagerie artificielle par un modèle coopératif multi-résolution / Franck Mangin (1994)PermalinkEin Beitrag zur kartographischen Mustererkennung mittels Methoden der Künstlichen Intelligenz / W. Lin (1994)PermalinkEstimation, modélisation et langage de déclaration et de manipulation de champs spatiaux continus / Dillon Pariente (1994)PermalinkRecherche d'outils et de représentations pour la généralisation / Emmanuel Fritsch (1994)PermalinkBase aérienne militaire et missions opérationnelles, Volume 1. Mémoire / Pascal Legai (1993)PermalinkBase aérienne militaire et missions opérationnelles, Volume 2. Annexes / Pascal Legai (1993)PermalinkBase aérienne militaire et missions opérationnelles, Volume 3. Annexes / Pascal Legai (1993)PermalinkBase aérienne militaire et missions opérationnelles, Volume 4. Projet Brainware / Pascal Legai (1993)PermalinkBase de données géographique et photos aériennes / Sylvie Servigne (1993)PermalinkIJCAI-93, proceedings of the 13th International Joint Conference on Artificial Intelligence, Chambéry, Savoie, France, 28 August - 3 September 1993, 2. Proceedings / Ruzena Bajcsy (1993)PermalinkParallel algorithms for digital image processing, computer vision and neural networks / I. Pitas (1993)PermalinkRéseaux de neurones / J.P. Nadal (1993)PermalinkGIS, LIS '92 Annual conference and exposition, November 10-12, 1992, San Jose, California, Volume 1. Proceedings / American society for photogrammetry and remote sensing (1992)PermalinkGIS, LIS '92 Annual conference and exposition, November 10-12, 1992, San Jose, California, Volume 2. Proceedings / American society for photogrammetry and remote sensing (1992)PermalinkNeural networks / Association des entretiens de Lyon (1990)Permalink