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Meta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 4 (April 2022) . - n° 5527913 Titre : Meta-learning based hyperspectral target detection using siamese network Type de document : Article/Communication Auteurs : Yulei Wang, Auteur ; Xi Chen, Auteur ; Fengchao Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5527913 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification pixellaire [Termes IGN] détection de cible [Termes IGN] espace euclidien [Termes IGN] filtrage numérique d'image [Termes IGN] image hyperspectrale [Termes IGN] réseau neuronal siamois [Termes IGN] triplet Résumé : (auteur) When predicting data for which limited supervised information is available, hyperspectral target detection methods based on deep transfer learning expect that the network will not require considerable retraining to generalize to unfamiliar application contexts. Meta-learning is an effective and practical framework for solving this problem in deep learning. This article proposes a new meta-learning based hyperspectral target detection using Siamese network (MLSN). First, a deep residual convolution feature embedding module is designed to embed spectral vectors into the Euclidean feature space. Then, the triplet loss is used to learn the intraclass similarity and interclass dissimilarity between spectra in embedding feature space by using the known labeled source data on the designed three-channel Siamese network for meta-training. The learned meta-knowledge is updated with the prior target spectrum through a designed two-channel Siamese network to quickly adapt to the new detection task. It should be noted that the parameters and structure of the deep residual convolution embedding modules of each channel in the Siamese network are identical. Finally, the spatial information is combined, and the detection map of the two-channel Siamese network is processed by the guiding image filtering and morphological closing operation, and a final detection result is obtained. Based on the experimental analysis of six real hyperspectral image datasets, the proposed MLSN has shown its excellent comprehensive performance. Numéro de notice : A2022-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2022.3169970 Date de publication en ligne : 22/04/2022 En ligne : https://doi.org/10.1109/TGRS.2022.3169970 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100649 [article]

A spatial model of cognitive distance in cities / Ed Manley in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2316 - 2338 Titre : A spatial model of cognitive distance in cities Type de document : Article/Communication Auteurs : Ed Manley, Auteur ; Gabriele Filomena, Auteur ; Panos Mavros, Auteur Année de publication : 2021 Article en page(s) : pp 2316 - 2338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] cognition [Termes IGN] distance [Termes IGN] espace euclidien [Termes IGN] espace urbain [Termes IGN] modélisation spatiale [Termes IGN] morphologie urbaine [Termes IGN] perception [Termes IGN] positionnement statique [Termes IGN] représentation mentale spatiale [Termes IGN] système d'information urbain Résumé : (auteur) Spatial cognition is fundamental to the behaviour and activity of humans in urban space. Humans perceive their environments with systematic biases and errors, and act upon these perceptions, which in turn form urban patterns of activity. These perceptions are influenced by a multitude of factors, many of them relating to the static urban form. Yet much of geographic analysis ignores the influence of urban form, instead referring most commonly to the Euclidean arrangement of space. In this paper, we propose a novel spatial modelling framework for estimating cognitive distance in urban space. This framework is constructed from a wealth of research describing the effect of environmental factors on distance estimation, and produces a quantitative estimate of the effect based on standard GIS data. Unlike other cost measures, the cognitive distance estimate integrates systematically observed distortions and biases in spatial cognition. As a proof-of-concept, the framework is implemented for 26 cities worldwide using open data, producing a novel comparative measure of ‘cognitive accessibility’. The paper concludes with a discussion of the potential of this approach in analysing and modelling urban systems, and outlines areas for further research. Numéro de notice : A2021-761 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1887488 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1887488 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98790 [article]

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Machine learning and geodesy: A survey / Jemil Butt in Journal of applied geodesy, vol 15 n° 2 (April 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 2 (April 2021) . - pp 117 - 133 Titre : Machine learning and geodesy: A survey Type de document : Article/Communication Auteurs : Jemil Butt, Auteur ; Andreas Wieser, Auteur ; Zan Gojcic, Auteur ; Caifa Zhou, Auteur Année de publication : 2021 Article en page(s) : pp 117 - 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse de données [Termes IGN] apprentissage automatique [Termes IGN] données géodésiques [Termes IGN] espace de Hilbert [Termes IGN] méthode fondée sur le noyau Résumé : (Auteur) The goal of classical geodetic data analysis is often to estimate distributional parameters like expected values and variances based on measurements that are subject to uncertainty due to unpredictable environmental effects and instrument specific noise. Its traditional roots and focus on analytical solutions at times require strong prior assumptions regarding problem specification and underlying probability distributions that preclude successful application in practical cases for which the goal is not regression in presence of Gaussian noise. Machine learning methods are more flexible with respect to assumed regularity of the input and the form of the desired outputs and allow for nonparametric stochastic models at the cost of substituting easily analyzable closed form solutions by numerical schemes. This article aims at examining common grounds of geodetic data analysis and machine learning and showcases applications of algorithms for supervised and unsupervised learning to tasks concerned with optimal estimation, signal separation, danger assessment and design of measurement strategies that occur frequently and naturally in geodesy. Numéro de notice : A2021-321 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0043 Date de publication en ligne : 20/02/2021 En ligne : https://doi.org/10.1515/jag-2020-0043 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97478 [article]

Fusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 1 (January 2021) . - pp 31 - 45 Titre : Fusion of ground penetrating radar and laser scanning for infrastructure mapping Type de document : Article/Communication Auteurs : Dominik Merkle, Auteur ; Carsten Frey, Auteur ; Alexander Reiterer, Auteur Année de publication : 2021 Article en page(s) : pp 31 - 45 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] base de données localisées 3D [Termes IGN] données lidar [Termes IGN] espace de Hilbert [Termes IGN] lasergrammétrie [Termes IGN] lever souterrain [Termes IGN] radar pénétrant GPR [Termes IGN] radargrammétrie [Termes IGN] réseau technique souterrain [Termes IGN] semis de points [Termes IGN] sous-sol [Termes IGN] surface du sol [Termes IGN] système de numérisation mobile Résumé : (auteur) Mobile mapping vehicles, equipped with cameras, laser scanners (in this paper referred to as light detection and ranging, LiDAR), and positioning systems are limited to acquiring surface data. However, in this paper, a method to fuse both LiDAR and 3D ground penetrating radar (GPR) data into consistent georeferenced point clouds is presented, allowing imaging both the surface and subsurface. Objects such as pipes, cables, and wall structures are made visible as point clouds by thresholding the GPR signal’s Hilbert envelope. The results are verified with existing utility maps. Varying soil conditions, clutter, and noise complicate a fully automatized approach. Topographic correction of the GPR data, by using the LiDAR data, ensures a consistent ground height. Moreover, this work shows that the LiDAR point cloud, as a reference, increases the interpretability of GPR data and allows measuring distances between above ground and subsurface structures. Numéro de notice : A2021-044 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2020-0004 Date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1515/jag-2020-0004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96771 [article]

Spheroidal spline interpolation and its application in geodesy / Mostafa Kiani in Geodesy and cartography, vol 46 n° 3 (October 2020)  

Public [article]  inGeodesy and cartography > vol 46 n° 3 (October 2020) . - pp 123 - 135 Titre : Spheroidal spline interpolation and its application in geodesy Type de document : Article/Communication Auteurs : Mostafa Kiani, Auteur ; Nabi Chegini, Auteur ; Abdolreza Safari, Auteur Année de publication : 2020 Article en page(s) : pp 123 - 135 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse harmonique [Termes IGN] espace de Hilbert [Termes IGN] fonction de Green [Termes IGN] fonction spline d'interpolation [Termes IGN] force de gravitation [Termes IGN] optimisation (mathématiques) [Termes IGN] sphèroïde Résumé : (auteur) The aim of this paper is to study the spline interpolation problem in spheroidal geometry. We follow the minimization of the norm of the iterated Beltrami-Laplace and consecutive iterated Helmholtz operators for all functions belong-ing to an appropriate Hilbert space defined on the spheroid. By exploiting surface Green’s functions, reproducing kernels for discrete Dirichlet and Neumann conditions are constructed in the spheroidal geometry. According to a complete system of surface spheroidal harmonics, generalized Green’s functions are also defined. Based on the minimization problem and corresponding reproducing kernel, spline interpolant which minimizes the desired norm and satisfies the given discrete conditions is defined on the spheroidal surface. The application of the results in Geodesy is explained in the gravity data interpolation over the globe. Numéro de notice : A2020-783 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3846/gac.2020.11316 En ligne : https://doi.org/10.3846/gac.2020.11316 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96479 [article]

Foundations of deep convolutional models through kernel methods / Alberto Bietti (2019)  

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Computing with cognitive spatial frames of reference in GIS / Simon Scheider in Transactions in GIS, vol 22 n° 5 (October 2018)  

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Panda∗: A generic and scalable framework for predictive spatio-temporal queries / Abdeltawab M. Hendawi in Geoinformatica, vol 21 n° 2 (April - June 2017)  

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A method for assessing generalized data accuracy with linear object resolution verification / Tadeusz Chrobak in Geocarto international, vol 32 n° 3 (March 2017)  

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Linear Algebra / Arak M. Mathai (2017)  

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Location K-anonymity in indoor spaces / Joon-Seok Kim in Geoinformatica, vol 20 n° 3 (July - September 2016)  

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Mappes et temporalités : la logique mappologique à l'épreuve de la topochronie / Régis Keerle in Cartes & Géomatique, n° 225 (septembre 2015) 

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On reverse-k-nearest-neighbor joins / Tobias Emrich in Geoinformatica, vol 19 n° 2 (April - June 2015)  

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Efficient continuous top-k spatial keyword queries on road networks / Long Guo in Geoinformatica, vol 19 n° 1 (January - March 2015)  

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3D Hilbert space filling curves in 3D city modeling for faster spatial queries / Uznir Ujang in International journal of 3-D information modeling, vol 3 n° 2 (April - June 2014)  

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