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Land cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models / Diego Marcos in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) Titre : Land cover mapping at very high resolution with rotation equivariant CNNs : Towards small yet accurate models Type de document : Article/Communication Auteurs : Diego Marcos, Auteur ; Michele Volpi, Auteur ; Benjamin Kellenberger, Auteur ; Devis Tuia, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Bade-Wurtemberg (Allemagne) [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] enrichissement sémantique [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs IGN] image à ultra haute résolution [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object’s orientation and on a sensor’s flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters. Numéro de notice : A2018-491 Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.021 date de publication en ligne : 19/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91227 [article]

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Label propagation with ensemble of pairwise geometric relations : towards robust large-scale retrieval of object instances / Xiaomeng Wu in International journal of computer vision, vol 126 n° 7 (July 2018)  

Public [article]  inInternational journal of computer vision > vol 126 n° 7 (July 2018) . - pp 689 - 713 Titre : Label propagation with ensemble of pairwise geometric relations : towards robust large-scale retrieval of object instances Type de document : Article/Communication Auteurs : Xiaomeng Wu, Auteur ; Kaoru Hiramatsu, Auteur ; Kunio Kashino, Auteur Année de publication : 2018 Article en page(s) : pp 689 - 713 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] instance [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] recherche d'image basée sur le contenu [Termes descripteurs IGN] relation spatiale Résumé : (Auteur) Spatial verification methods permit geometrically stable image matching, but still involve a difficult trade-off between robustness as regards incorrect rejection of true correspondences and discriminative power in terms of mismatches. To address this issue, we ask whether an ensemble of weak geometric constraints that correlates with visual similarity only slightly better than a bag-of-visual-words model performs better than a single strong constraint. We consider a family of spatial verification methods and decompose them into fundamental constraints imposed on pairs of feature correspondences. Encompassing such constraints leads us to propose a new method, which takes the best of existing techniques and functions as a unified Ensemble of pAirwise GEometric Relations (EAGER), in terms of both spatial contexts and between-image transformations. We also introduce a novel and robust reranking method, in which the object instances localized by EAGER in high-ranked database images are reissued as new queries. EAGER is extended to develop a smoothness constraint where the similarity between the optimized ranking scores of two instances should be maximally consistent with their geometrically constrained similarity. Reranking is newly formulated as two label propagation problems: one is to assess the confidence of new queries and the other to aggregate new independently executed retrievals. Extensive experiments conducted on four datasets show that EAGER and our reranking method outperform most of their state-of-the-art counterparts, especially when large-scale visual vocabularies are used. Numéro de notice : A2018-411 Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-018-1063-9 date de publication en ligne : 31/01/2018 En ligne : https://doi.org/10.1007/s11263-018-1063-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90886 [article]

Large-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net / Timo Hackel in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 5 (mai 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 5 (mai 2018) . - pp 297 - 308 Titre : Large-scale supervised learning for 3D Point cloud labeling : Semantic3d.Net Type de document : Article/Communication Auteurs : Timo Hackel, Auteur ; Jan Dirk Wegner, Auteur ; Nikolay Savinov, Auteur ; Lubor Ladicky, Auteur ; Konrad Schindler, Auteur ; Marc Pollefeys, Auteur Année de publication : 2018 Article en page(s) : pp 297 - 308 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] état de l'art [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] semis de points Résumé : (Auteur) In this paper, we review current state-of-the-art in 3D point cloud classification, present a new 3D point cloud classification benchmark data set of single scans with over four billion manually labeled points, and discuss first available results on the benchmark. Much of the stunning recent progress in 2D image interpretation can be attributed to the availability of large amounts of training data, which have enabled the (supervised) learning of deep neural networks. With the data set presented in this paper, we aim to boost the performance of CNNs also for 3D point cloud labeling. Our hope is that this will lead to a breakthrough of deep learning also for 3D (geo-) data. The semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains eight semantic classes and covers a wide range of urban outdoor scenes, including churches, streets, railroad tracks, squares, villages, soccer fields, and castles. We describe our labeling interface and show that, compared to those already available to the research community, our data set provides denser and more complete point clouds, with a much higher overall number of labeled points. We further provide descriptions of baseline methods and of the first independent submissions, which are indeed based on CNNs, and already show remarkable improvements over prior art. We hope that semantic3D.net will pave the way for deep learning in 3D point cloud analysis, and for 3D representation learning in general. Numéro de notice : A2018-162 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.5.297 date de publication en ligne : 01/05/2018 En ligne : https://doi.org/10.14358/PERS.84.5.297 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89795 [article]

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Open land cover from OpenStreetMap and remote sensing / Michael Schultz in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 63 (December 2017) . - pp 206 - 213 Titre : Open land cover from OpenStreetMap and remote sensing Type de document : Article/Communication Auteurs : Michael Schultz, Auteur ; Janek Voss, Auteur ; Michael Auer, Auteur ; Sarah Carter, Auteur ; Alexander Zipf, Auteur Année de publication : 2017 Article en page(s) : pp 206 - 213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] Heidelberg [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] OpenStreetMap Résumé : (auteur) OpenStreetMap (OSM) tags were used to produce a global Open Land Cover (OLC) product with fractional data gaps available at osmlanduse.org. Data gaps in the global OLC map were filled for a case study in Heidelberg, Germany using free remote sensing data, which resulted in a land cover (LC) prototype with complete coverage in this area. Sixty tags in the OSM were used to allocate a Corine Land Cover (CLC) level 2 land use classification to 91.8% of the study area, and the remaining gaps were filled with remote sensing data. For this case study, complete are coverage OLC overall accuracy was estimated 87%, which performed better than the CLC product (81% overall accuracy) of 2012. Spatial thematic overlap for the two products was 84%. OLC was in large parts found to be more detailed than CLC, particularly when LC patterns were heterogeneous, and outperformed CLC in the classification of 12 of the 14 classes. Our OLC product represented data created in different periods; 53% of the area was 2011–2016, and 46% of the area was representative of 2016–2017. Numéro de notice : A2017-638 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.07.014 En ligne : https://doi.org/10.1016/j.jag.2017.07.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86989 [article]

A novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3071 - 3083 Titre : A novel semisupervised active-learning algorithm for hyperspectral image classification Type de document : Article/Communication Auteurs : Zengmao Wang, Auteur ; Bo Du, Auteur ; Lefei Zhang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 3071 - 3083 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) Less training samples are a challenging problem in hyperspectral image classification. Active learning and semisupervised learning are two promising techniques to address the problem. Active learning solves the problem by improving the quality of the training samples, while semisupervised learning solves the problem by increasing the quantity of the training samples. However, they pay too much attention to the discriminative information in the unlabeled data, leading to information bias to train supervised models, and much more effort to label samples. Therefore, a method to discover representativeness and discriminativeness by semisupervised active learning is proposed. It takes advantages of both active learning and semisupervised learning. The representativeness and discriminativeness are discovered with a labeling process based on a supervised clustering technique and classification results. Specifically, the supervised clustering results can discover important structural information in the unlabeled data, and the classification results are also highly confidential in the active-learning process. With these clustering results and classification results, we can assign pseudolabels to the unlabeled data. Meanwhile, the unlabeled samples that cannot be assigned with pseudolabels with high confidence at each iteration are regarded as candidates in active learning. The methodology is validated on four hyperspectral data sets. Significant improvements in classification accuracy are achieved by the proposed method with respect to the state-of-the-art methods. Numéro de notice : A2017-473 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2650938 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86398 [article]

Mixed map labeling / Maarten Löffler in Journal of Spatial Information Science (JoSIS), n° 13 (September 2016)  

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Label embedding : a frugal baseline for text recognition / Jose A. Rodriguez-Serrano in International journal of computer vision, vol 113 n° 3 (July 2015)  

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Confounds and Consequences in Geotagged Twitter Data / Umashanthi Pavalanathan (7/06/2015)    

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Génération de modèles numériques de surface et détection de changements 3D à partir d'imagerie satellite stéréoscopique très haute résolution / Cyrielle Guerin (2014)  

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Assessing the veracity of methods for extracting place semantics from Flickr tags / William A Mackaness in Transactions in GIS, vol 17 n° 4 (August 2013)

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Finding science with science: Evaluating a domain and scientific ontology user interface for the discovery of scientific resources / Kristin Stock in Transactions in GIS, vol 17 n° 4 (August 2013)

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The annotation process in OpenStreetMap / P. Mooney in Transactions in GIS, vol 16 n° 4 (August 2012)

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Object-based image analysis of high-resolution satellite images using modified cloud basis function neural network and probabilistic relaxation labeling process / A. Rizvi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 12 Tome 1 (December 2011)

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vol 16 n° 4 - juillet - août 2011 - Système d'information mobiquitaires (Bulletin de Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI) / Serge Miranda  

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