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Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)
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Titre : Implementing Moran eigenvector spatial filtering for massively large georeferenced datasets Type de document : Article/Communication Auteurs : Daniel A. Griffith, Auteur ; Yongwan Chun, Auteur Année de publication : 2019 Article en page(s) : pp 1703 - 1717 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approximation
[Termes IGN] autocorrélation spatiale
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] image Landsat-TM
[Termes IGN] régression linéaire
[Termes IGN] segmentation d'image
[Termes IGN] tessellation
[Termes IGN] vecteur propreMots-clés libres : Moran eigenvector spatial filtering Résumé : (auteur) Moran eigenvector spatial filtering (MESF) furnishes an alternative method to account for spatial autocorrelation in linear regression specifications describing georeferenced data, although spatial auto-models also are widely used. The utility of this MESF methodology is even more impressive for the non-Gaussian models because its flexible structure enables it to be easily applied to generalized linear models, which include Poisson, binomial, and negative binomial regression. However, the implementation of MESF can be computationally challenging, especially when the number of geographic units, n, is large, or massive, such as with a remotely sensed image. This intensive computation aspect has been a drawback to the use of MESF, particularly for analyzing a remotely sensed image, which can easily contain millions of pixels. Motivated by Curry, this paper proposes an approximation approach to constructing eigenvector spatial filters (ESFs) for a large spatial tessellation. This approximation is based on a divide-and-conquer approach. That is, it constructs ESFs separately for each sub-region, and then combines the resulting ESFs across an entire remotely sensed image. This paper, employing selected specimen remotely sensed images, demonstrates that the proposed technique provides a computationally efficient and successful approach to implement MESF for large or massive spatial tessellations. Numéro de notice : A2019-388 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1080/13658816.2019.1593421 Date de publication en ligne : 02/04/2019 En ligne : https://doi.org/10.1080/13658816.2019.1593421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93479
in International journal of geographical information science IJGIS > vol 33 n° 9 (September 2019) . - pp 1703 - 1717[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 079-2019092 RAB Revue Centre de documentation En réserve L003 Disponible Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)
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Titre : Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain Type de document : Article/Communication Auteurs : Yolanda Torres, Auteur ; José Juan Arranz, Auteur ; Jorge M. Gaspar-Escribano, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 161-175 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] données lidar
[Termes IGN] empreinte
[Termes IGN] Espagne
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] hauteur du bâti
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] orthophotographie
[Termes IGN] risque urbain
[Termes IGN] segmentation d'image
[Termes IGN] séisme
[Termes IGN] stratification de données
[Termes IGN] vulnérabilité
[Termes IGN] zone à risqueRésumé : (auteur) We present a procedure for assessing the urban exposure and seismic vulnerability that integrates LiDAR data with aerial images from the Spanish National Plan of Aerial Orthophotography (PNOA). It comprises three phases: first, we segment the satellite image to divide the study area into different urban patterns. Second, we extract building footprints and attributes that represent the type of building of each urban pattern. Finally, we assign the seismic vulnerability to each building using different machine-learning techniques: Decision trees, SVM, logistic regression and Bayesian networks. We apply the procedure to 826 buildings in the city of Lorca (SE Spain), where we count on a vulnerability database that we use as ground truth for the validation of results. The outcomes show that the machine learning techniques have similar performance, yielding vulnerability classification results with an accuracy of 77%–80% (F1-Score). The procedure is scalable and can be replicated in different areas. This is particularly relevant in Spain, where more than seven hundred towns have to develop seismic risk studies in the years to come, according to the General Direction of Civil Protection and Emergencies. It is especially interesting as a complement to conventional data gathering approaches for disaster risk applications in cities where field surveys need to be restricted to certain areas, dates or budget. Numéro de notice : A2019-471 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.05.015 Date de publication en ligne : 25/05/2019 En ligne : https://doi.org/10.1016/j.jag.2019.05.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93617
in International journal of applied Earth observation and geoinformation > vol 81 (September 2019) . - pp 161-175[article]Learning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)
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Titre : Learning and adapting robust features for satellite image segmentation on heterogeneous data sets Type de document : Article/Communication Auteurs : Sina Ghassemi, Auteur ; Attilio Friandrotti, Auteur ; Gianluca Francini, Auteur ; Enrico Magli, Auteur Année de publication : 2019 Article en page(s) : pp 6517 - 6529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] coût
[Termes IGN] données hétérogènes
[Termes IGN] image binaire
[Termes IGN] image satellite
[Termes IGN] méthode robuste
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation binaire
[Termes IGN] segmentation d'image
[Termes IGN] test de performanceRésumé : (auteur) This paper addresses the problem of training a deep neural network for satellite image segmentation so that it can be deployed over images whose statistics differ from those used for training. For example, in postdisaster damage assessment, the tight time constraints make it impractical to train a network from scratch for each image to be segmented. We propose a convolutional encoder–decoder network able to learn visual representations of increasing semantic level as its depth increases, allowing it to generalize over a wider range of satellite images. Then, we propose two additional methods to improve the network performance over each specific image to be segmented. First, we observe that updating the batch normalization layers’ statistics over the target image improves the network performance without human intervention. Second, we show that refining a trained network over a few samples of the image boosts the network performance with minimal human intervention. We evaluate our architecture over three data sets of satellite images, showing the state-of-the-art performance in binary segmentation of previously unseen images and competitive performance with respect to more complex techniques in a multiclass segmentation task. Numéro de notice : A2019-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906689 Date de publication en ligne : 17/04/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2906689 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93379
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6517 - 6529[article]Preparing the holoLens for user studies : an augmented reality interface for the spatial adjustment of holographic objects in 3D indoor environments / Julian Keil in KN, Journal of Cartography and Geographic Information, vol 69 n° 3 (September 2019)
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Titre : Preparing the holoLens for user studies : an augmented reality interface for the spatial adjustment of holographic objects in 3D indoor environments Type de document : Article/Communication Auteurs : Julian Keil, Auteur ; Dennis Edler, Auteur ; Frank Dickmann, Auteur Année de publication : 2019 Article en page(s) : pp 205 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] espace intérieur
[Termes IGN] hologramme
[Termes IGN] holographie
[Termes IGN] interface graphique
[Termes IGN] réalité augmentée
[Termes IGN] représentation mentale spatiale
[Termes IGN] scène 3DRésumé : (Auteur) Augmented reality (AR), the extension of the real physical world with holographic objects provides numerous ways to influence how people perceive and interact with geographic space. Such holographic elements may for example improve orientation, navigation, and the mental representations of space generated through interaction with the environment. As AR hardware is still in an early development stage, scientific investigations of the effects of holographic elements on spatial knowledge and perception are fundamental for the development of user-oriented AR applications. However, accurate and replicable positioning of holograms in real world space, a highly relevant precondition for standardized scientific experiments on spatial cognition, is still an issue to be resolved. In this paper, we specify technical causes for this limitation. Subsequently, we describe the development of a Unity-based AR interface capable of adding, selecting, placing and removing holograms. The capability to quickly reposition holograms compensates for the lack of hologram stability and enables the implementation of AR-based geospatial experiments and applications. To facilitate the implementation of other task-oriented AR interfaces, code examples are provided and commented. Numéro de notice : A2019-458 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s42489-019-00025-z Date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1007/s42489-019-00025-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93549
in KN, Journal of Cartography and Geographic Information > vol 69 n° 3 (September 2019) . - pp 205 - 215[article]Sentinel-2 sharpening using a reduced-rank method / Magnus Orn Ulfarsson in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)
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Titre : Sentinel-2 sharpening using a reduced-rank method Type de document : Article/Communication Auteurs : Magnus Orn Ulfarsson, Auteur ; Frosti Palsson, Auteur ; Mauro Dalla Mura, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2019 Article en page(s) : pp 6408 - 6420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] affinage d'image
[Termes IGN] ajustement de paramètres
[Termes IGN] estimation bayesienne
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] largeur de bandeRésumé : (auteur) Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60 m. In many remote sensing applications, the availability of images at the highest spatial resolution (i.e., 10 m for S2) is often desirable. This can be achieved by generating a synthetic high-resolution image through data fusion. To this end, researchers have proposed techniques exploiting the spectral/spatial correlation inherent in multispectral data to sharpen the lower resolution S2 bands to 10 m. In this paper, we propose a novel method that formulates the sharpening process as a solution to an inverse problem. We develop a cyclic descent algorithm called S2Sharp and an associated tuning parameter selection algorithm based on generalized cross validation and Bayesian optimization. The tuning parameter selection method is evaluated on a simulated data set. The effectiveness of S2Sharp is assessed experimentally by comparisons to state-of-the-art methods using both simulated and real data sets. Numéro de notice : A2019-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906048 Date de publication en ligne : 22/04/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2906048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93377
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6408 - 6420[article]Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition / Sheng Wang in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
PermalinkIndividual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])
PermalinkImproving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
PermalinkLocal climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
PermalinkUne nouvelle méthode de vectorisation du cadastre ancien / Antony Chalais in Géomatique expert, n° 129 (août - septembre 2019)
PermalinkPyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
PermalinkMapping the wavelength position of mineral features in hyperspectral thermal infrared data / Christoph Hecker in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)
PermalinkOcclusion probability in operational forest inventory field sampling with ForeStereo / Fernando Montes in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)
PermalinkStructural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)
PermalinkSemantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)
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