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Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution / Zhenfeng Shao in Geo-spatial Information Science, vol 25 n° 4 (December 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 4 (December 2022) . - pp 550 - 567 Titre : Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution Type de document : Article/Communication Auteurs : Zhenfeng Shao, Auteur ; Yuan Zhang, Auteur ; Cheng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 550 - 567 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] approche hiérarchique [Termes IGN] Chine [Termes IGN] distribution spatiale [Termes IGN] image Gaofen [Termes IGN] image Landsat-OLI [Termes IGN] scène urbaine [Termes IGN] surface imperméable Résumé : (auteur) Impervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas. Numéro de notice : A2022-890 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2028535 Date de publication en ligne : 02/03/2022 En ligne : https://doi.org/10.1080/10095020.2022.2028535 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102237 [article]

A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions / Rasit Ulug in Journal of geodesy, vol 96 n° 12 (December 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 12 (December 2022) . - n° 91 Titre : A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions Type de document : Article/Communication Auteurs : Rasit Ulug, Auteur ; Mahmut Onur Karslıoglu, Auteur Année de publication : 2022 Article en page(s) : n° 91 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse de groupement [Termes IGN] Auvergne [Termes IGN] centroïde [Termes IGN] champ de pesanteur local [Termes IGN] champ de pesanteur terrestre [Termes IGN] classification barycentrique [Termes IGN] classification ISODATA [Termes IGN] Colorado (Etats-Unis) [Termes IGN] fonction de base radiale [Termes IGN] largeur de bande [Termes IGN] modèle de géopotentiel local [Termes IGN] modèle numérique de terrain Résumé : (auteur) In this study, a new data-adaptive network design methodology called k-SRBF is presented for the spherical radial basis functions (SRBFs) in regional gravity field modeling. In this methodology, the cluster centers (centroids) obtained by the k-means clustering algorithm are post-processed to construct a network of SRBFs by replacing the centroids with the SRBFs. The post-processing procedure is inspired by the heuristic method, Iterative Self-Organizing Data Analysis Technique (ISODATA), which splits clusters within the user-defined criteria to avoid over- and under-parameterization. These criteria are the minimum spherical distance between the centroids and the minimum number of samples for each cluster. The bandwidth (depth) of each SRBF is determined using the generalized cross-validation (GCV) technique in which only the observations within the radius of impact area (RIA) are used. The numerical tests are carried out with real and simulated data sets to investigate the effect of the user-defined criteria on the network design. Different bandwidth limits are also examined, and the appropriate lower and upper bandwidth limits are chosen based on the empirical signal covariance function and user-defined criteria. Also, additional tests are performed to verify the performance of the proposed methodology in combining different types of observations, such as terrestrial and airborne data available in Colorado. The results reveal that k-SRBF is an effective methodology to establish a data-adaptive network for SRBFs. Moreover, the proposed methodology improves the condition number of normal equation matrix so that the least-squares procedure can be applied without regularization considering the user-defined criteria and bandwidth limits. Numéro de notice : A2022-877 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01681-2 Date de publication en ligne : 22/11/2022 En ligne : https://doi.org/10.1007/s00190-022-01681-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102190 [article]

Reconstructing compact building models from point clouds using deep implicit fields / Zhaiyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 194 (December 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 194 (December 2022) . - pp 58 - 73 Titre : Reconstructing compact building models from point clouds using deep implicit fields Type de document : Article/Communication Auteurs : Zhaiyu Chen, Auteur ; Hugo Ledoux, Auteur ; Seyran Khademi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 58 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] Bâti-3D [Termes IGN] champ aléatoire de Markov [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de modèle [Termes IGN] image à haute résolution [Termes IGN] maillage par triangles [Termes IGN] optimisation (mathématiques) [Termes IGN] polygone [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points Résumé : (auteur) While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open problem. In this paper, we present a novel framework for reconstructing compact, watertight, polygonal building models from point clouds. Our framework comprises three components: (a) a cell complex is generated via adaptive space partitioning that provides a polyhedral embedding as the candidate set; (b) an implicit field is learned by a deep neural network that facilitates building occupancy estimation; (c) a Markov random field is formulated to extract the outer surface of a building via combinatorial optimization. We evaluate and compare our method with state-of-the-art methods in generic reconstruction, model-based reconstruction, geometry simplification, and primitive assembly. Experiments on both synthetic and real-world point clouds have demonstrated that, with our neural-guided strategy, high-quality building models can be obtained with significant advantages in fidelity, compactness, and computational efficiency. Our method also shows robustness to noise and insufficient measurements, and it can directly generalize from synthetic scans to real-world measurements. The source code of this work is freely available at https://github.com/chenzhaiyu/points2poly. Numéro de notice : A2022-824 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.09.017 Date de publication en ligne : 17/10/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.09.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102001 [article]

Relevé 2D & 3D du marégraphe de Marseille / Emmanuel Clédat in XYZ, n° 173 (décembre 2022)

Public [article]  inXYZ > n° 173 (décembre 2022) . - pp 55 - 62 Titre : Relevé 2D & 3D du marégraphe de Marseille Type de document : Article/Communication Auteurs : Emmanuel Clédat , Auteur ; Clovis Bergeret, Auteur ; Marius Dahuron, Auteur ; Lilian Wecker, Auteur ; Frédéric Ye, Auteur Année de publication : 2022 Article en page(s) : pp 55 - 62 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] géoréférencement [Termes IGN] image captée par drone [Termes IGN] marégraphe [Termes IGN] Marseille [Termes IGN] modélisation 2D [Termes IGN] modélisation 3D [Termes IGN] semis de points Résumé : (Editeur) Le marégraphe de Marseille est un monument historique de l’IGN. Pour permettre au plus grand nombre de le visiter (virtuellement), et pour préparer d’éventuels travaux de restauration, l’association des amis du marégraphe a commandité une modélisation 3D. Effectués par les élèves de l’ENSG-Géomatique en utilisant les méthodes de photogrammétrie et de scanner laser terrestre, ces relevés ont permis de produire un modèle 3D intérieur et extérieur, mais aussi des produits 2D : coupes, plans, écorchés. Numéro de notice : A2022-912 Affiliation des auteurs : ENSG (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102270 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
112-2022041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach / Hakan Oktay Aydınlı in Applied geomatics, vol 14 n° 4 (December 2022)  

Public [article]  inApplied geomatics > vol 14 n° 4 (December 2022) . - pp 669 - 678 Titre : Sea surface temperature prediction model for the Black Sea by employing time-series satellite data: a machine learning approach Type de document : Article/Communication Auteurs : Hakan Oktay Aydınlı, Auteur ; Ali Ekincek, Auteur ; Mervegül Aykanat-Atay, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 669 - 678 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal récurrent [Termes IGN] détection de changement [Termes IGN] données Copernicus [Termes IGN] image Aqua-MODIS [Termes IGN] méthode des moindres carrés [Termes IGN] modèle de simulation [Termes IGN] Noire, mer [Termes IGN] optimisation (mathématiques) [Termes IGN] série temporelle [Termes IGN] température de surface de la mer Résumé : (auteur) High temporal resolution remote sensing images provide continuous data about the marine environment, which is critical for gaining extensive knowledge about the aquatic environment and marine species. Sea surface temperature (SST) is one of the basic parameters that can be obtained with the help of remote sensing. Long-term alterations in the SST can affect the aquatic environment and marine species, such as the life expectancy of anchovies in the Black Sea. Forecasting the dynamics of SSTs is crucial for detecting and eliminating the SST-oriented impacts. The goal of the current study is to construct a predictive model to estimate the daily SST value for the mid-Black Sea using a machine learning approach by employing time-series satellite data from 2008 to 2021. Turkey’s mid-Black Sea coastal line, comprising Ordu, Samsun, and Sinop stations, was chosen as the study area. The SST predictive model was represented by applying the recurrent neural network (RNN) long- and short-term memory (LSTM). Adam stochastic optimization was used for validation, and the mean square error (MSE) for each location was found to be 0.914, 0.815, and 0.802, respectively. The findings indicate that our model is significantly promising for accurate and effective short- and midterm daily SST prediction. Numéro de notice : A2022-894 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-022-00462-y Date de publication en ligne : 23/08/2022 En ligne : https://doi.org/10.1007/s12518-022-00462-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102242 [article]

A semi-automatic method for extraction of urban features by integrating aerial images and LIDAR data and comparing its performance in areas with different feature structures (case study: comparison of the method performance in Isfahan and Toronto) / Masoud Azad in Applied geomatics, vol 14 n° 4 (December 2022)  

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Vine canopy reconstruction and assessment with terrestrial Lidar and aerial imaging / Igor Petrovic in Remote sensing, vol 14 n° 22 (November-2 2022)  

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3D target detection using dual domain attention and SIFT operator in indoor scenes / Hanshuo Zhao in The Visual Computer, vol 38 n° 11 (November 2022)  

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An unsupervised framework for extracting multilane roads from OpenStreetMap / Kunkun Wu in International journal of geographical information science IJGIS, vol 36 n° 11 (November 2022)  

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Automatic vectorization of fluvial corridor features on historical maps to assess riverscape changes / Samuel Dunesme in Cartography and Geographic Information Science, vol 49 n° 6 (November 2022)  

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Change alignment-based image transformation for unsupervised heterogeneous change detection / Kuowei Xiao in Remote sensing, vol 14 n° 21 (November-1 2022)  

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Cross-guided pyramid attention-based residual hyperdense network for hyperspectral image pansharpening / Jiahui Qu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 11 (November 2022)  

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Evaluation of softwood timber quality: A case study on two silvicultural systems in Central Germany / Kristen Höwler in Forests, vol 13 n° 11 (November 2022)  

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Foreground-aware refinement network for building extraction from remote sensing images / Zhang Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 11 (November 2022)  

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GA-Net: A geometry prior assisted neural network for road extraction / Xin Chen in International journal of applied Earth observation and geoinformation, vol 114 (November 2022)  

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