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Superpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)
Titre : Superpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks Type de document : Article/Communication Auteurs : Tristan Postadjian , Auteur ; Arnaud Le Bris , Auteur ; Hichem Sahbi, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : GeoSud / Conférence : IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium, observing, understanding and forecasting the dynamics of our planet 22/07/2018 27/07/2018 Valencia Espagne Proceedings IEEE Importance : pp 1328 - 1331 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données topographiques
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification pixellaire
[Termes IGN] image à très haute résolution
[Termes IGN] image infrarouge
[Termes IGN] image RVB
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation d'imageRésumé : (auteur) Supervised classification is the fundamental task for landcover map generation. Deep neural networks recently outperformed other state-of-the-art classifiers in many machine learning challenges, from semantic segmentation to speech recognition. Such strategies are now commonly employed in the literature for the purpose of land-cover mapping. This paper develops the strategy for the use of deep networks to label very high resolution satellite images, with the perspective of mapping regions at country scale. Therefore, a superpixel based method is introduced in order to (i) ensure correct delineation of objects and (ii) perform the classification in a dense way but with decent computing times. Numéro de notice : C2018-056 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8519222 Date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8519222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91370 SuperPoint Graph : segmentation sémantique de nuages de points LiDAR à grande échelle / Loïc Landrieu (2018)
contenu dans 27èmes Journées de la Recherche de l'IGN / Journées Recherche de l'IGN 2018, 27es Journées (22 - 23 mars 2018; Cité Descartes, Champs-sur-Marne, France) (2018)
Titre : SuperPoint Graph : segmentation sémantique de nuages de points LiDAR à grande échelle Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Martin Simonovsky, Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2018 Conférence : Journées Recherche de l'IGN 2018, 27es Journées 22/03/2018 23/03/2018 Champs-sur-Marne France programme sans actes Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] réseau neuronal artificiel
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) SuperPoint Graph est un nouvel algorithme permettant la sémantisation précise de très grands volumes de nuages de points acquis par LiDAR. Il repose sur une partition du nuage en formes simples à l'aide d'un modèle d'énergie globale, qui permet de réduire considérablement la taille et la complexité des entrées. Une représentation profonde de chaque forme est obtenue grâce à un réseau de neurones spécialisé dans le traitement de petits nuages de points. Enfin, un réseau de réseaux de neurones récurrents spatialement structuré permet d'exploiter les relations contextuelles entre formes. La précision des résultats obtenus a permis à SuperPoint Graph de se hisser à la tête de plusieurs benchmarks internationaux. Numéro de notice : C2018-088 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91540 Documents numériques
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SuperPoint Graph - diaporama de présentationAdobe Acrobat PDF Toponym matching through deep neural networks / Rui Santos in International journal of geographical information science IJGIS, vol 32 n° 1-2 (January - February 2018)
[article]
Titre : Toponym matching through deep neural networks Type de document : Article/Communication Auteurs : Rui Santos, Auteur ; Patricia Murrieta-Flores, Auteur ; Pavel Calado, Auteur ; Bruno Martins, Auteur Année de publication : 2018 Article en page(s) : pp 324 - 348 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie
[Termes IGN] appariement
[Termes IGN] apprentissage profond
[Termes IGN] recherche d'information géographique
[Termes IGN] répertoire toponymique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] similitude sémantique
[Termes IGN] toponyme
[Termes IGN] traitement de données localiséesRésumé : (Auteur) Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics. Numéro de notice : A2018-027 Affiliation des auteurs : non IGN Thématique : TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1390119 En ligne : https://doi.org/10.1080/13658816.2017.1390119 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89179
in International journal of geographical information science IJGIS > vol 32 n° 1-2 (January - February 2018) . - pp 324 - 348[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018011 RAB Revue Centre de documentation En réserve L003 Disponible 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]Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network / Wei Zhao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)
[article]
Titre : Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network Type de document : Article/Communication Auteurs : Wei Zhao, Auteur ; Zhirui Wang, Auteur ; Maoguo Gong, Auteur ; Jia Liu, Auteur Année de publication : 2017 Article en page(s) : pp 7066 - 7080 Note générale : Bibliograpie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changementRésumé : (Auteur) With the application requirement, the technique for change detection based on heterogeneous remote sensing images is paid more attention. However, detecting changes between two heterogeneous images is challenging as they cannot be compared in low-dimensional space. In this paper, we construct an approximately symmetric deep neural network with two sides containing the same number of coupled layers to transform the two images into the same feature space. The two images are connected with the two sides and transformed into the same feature space, in which their features are more discriminative and the difference image can be generated by comparing paired features pixel by pixel. The network is first built by stacked restricted Boltzmann machines, and then, the parameters are updated in a special way based on clustering. The special way, motivated by that two heterogeneous images share the same reality in unchanged areas and retain respective properties in changed areas, shrinks the distance between paired features transformed from unchanged positions, and enlarges the distance between paired features extracted from changed positions. It is achieved through introducing two types of labels and updating parameters by adaptively changed learning rate. This is different from the existing methods based on deep learning that just do operations on positions predicted to be unchanged and extract only one type of labels. The whole process is completely unsupervised without any priori knowledge. Besides, the method can also be applied to homogeneous images. We test our method on heterogeneous images and homogeneous images. The proposed method achieves quite high accuracy. Numéro de notice : A2017-768 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2739800 En ligne : https://doi.org/10.1109/TGRS.2017.2739800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88807
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 7066 - 7080[article]High-resolution aerial image labeling with convolutional neural networks / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (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)PermalinkPermalinkUnsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification / Yiting Tao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkHybrid image noise reduction algorithm based on genetic ant colony and PCNN / Chong Shen in The Visual Computer, vol 33 n° 11 (November 2017)PermalinkAtmospheric correction over coastal waters using multilayer neural networks / Yongzhen Fan in Remote sensing of environment, vol 199 (15 September 2017)PermalinkForest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkRecurrent neural networks to correct satellite image classification maps / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkSDE: A novel selective, discriminative and equalizing feature representation for visual recognition / Guo-Sen Xie in International journal of computer vision, vol 124 n° 2 (1 September 2017)PermalinkSIG et intelligence artificielle : quels développements et quel futur ? / Christian Carolin in Géomatique expert, n° 118 (septembre - octobre 2017)PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang 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 relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkLearning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkInvestigating the potential of deep neural networks for large-scale classification of very high resolution satellite images / Tristan Postadjian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)PermalinkDeep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkAmélioration de la vitesse et de la qualité d'image du rendu basé image / Rodrigo Ortiz Cayón (2017)PermalinkPermalink