Descripteur
Documents disponibles dans cette catégorie (134)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)
[article]
Titre : X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data Type de document : Article/Communication Auteurs : Danfeng Hong, Auteur ; Naoto Yokoya, Auteur ; Gui-Song Sia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 12 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] bruit blanc
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] scène urbaine
[Termes IGN] transmission de donnéesRésumé : (auteur) This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Numéro de notice : A2020-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.014 Date de publication en ligne : 11/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95770
in ISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 12 - 23[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Geometric distortion of historical images for 3D visualization / Evelyn Paiz-Reyes in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
[article]
Titre : Geometric distortion of historical images for 3D visualization Type de document : Article/Communication Auteurs : Evelyn Paiz-Reyes , Auteur ; Mathieu Brédif , Auteur ; Sidonie Christophe , Auteur Année de publication : 2020 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 649 - 655 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données historiques
[Termes IGN] distorsion d'image
[Termes IGN] image numérisée
[Termes IGN] modèle de déformation des images
[Termes IGN] rendu (géovisualisation)
[Termes IGN] scène 3D
[Termes IGN] visualisation 3D
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Archivists, historians and national mapping agencies, among others, are archiving large datasets of historical photographs. Nevertheless, the capturing devices used to acquire these images possessed a diversity of effects that influenced the quality of the final resulting picture, e.g. geometric distortion, chromatic aberration, depth of field variation, etc. This paper examines singularly the topic of geometric distortion for a co-visualization of historical photos within a 3D model of the photographed scene. A distortion function of an image is ordinarily estimated only on the image domain by adjusting its parameters to observations of point correspondences. This mathematical function may exhibit overfits, oscillations or may not be well defined outside of this domain. The contribution of this work is the description of a distortion model defined on the whole undistorted image plane. We extrapolate the distortion estimated only on the image domain and then transfer this distortion information to the view of the 3D scene. This enables to look at the scene through an estimated camera and zoom out to see the context around the original photograph with a well-defined and behaved distortion. These findings may be a significant addition to the overall purpose of creating innovative ways to examine and visualize old photographs. Numéro de notice : A2020-468 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-649-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-649-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95537
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 649 - 655[article]Semi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)
[article]
Titre : Semi-automatic identification of submarine pipelines with synthetic aperture sonar Images Type de document : Article/Communication Auteurs : Victor Hugo Fernandes, Auteur ; Nilcilene Das Graças Medeiros, Auteur ; Dalto Domingues Rodrigues, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 376 - 395 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] canalisation
[Termes IGN] détection de contours
[Termes IGN] filtrage du bruit
[Termes IGN] scène sous-marine
[Termes IGN] sonarRésumé : (Auteur) Synthetic Aperture Sonar (SAS) is a sensor that was designed for hydrographic survey of the seabed. It detects small targets, enabling high geometric resolution images. However, the images generated by SAS are susceptible to speckle noise, which makes digital processing difficult, since the noises are confused with the targets of interest. The goal of this study was to develop a semi-automatic routine for SAS image processing to verify the structural integrity of submarine pipelines. The method presented incorporates four stages: pre-processing to reduce noise and highlight the targets of interest; extraction of features aiming to recognize features related to the pipelines; post-processing to reduce the fragmentation generated during feature extraction; validation to quantify the results from the reference images to estimate the performance of the proposed methodology. The results showed that more than 80% of the submarine pipelines were mapped in the semi-automatic mode, which considerably reduced the time needed to manually identify a large number of pipelines operating on offshore oilfields. Numéro de notice : A2020-248 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01490419.2020.1755916 Date de publication en ligne : 25/04/2020 En ligne : https://doi.org/10.1080/01490419.2020.1755916 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95362
in Marine geodesy > Vol 43 n° 4 (July 2020) . - pp 376 - 395[article]An Illumination Insensitive descriptor combining the CSLBP features for street view images in augmented reality: experimental studies / Zejun Xiang in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)
[article]
Titre : An Illumination Insensitive descriptor combining the CSLBP features for street view images in augmented reality: experimental studies Type de document : Article/Communication Auteurs : Zejun Xiang, Auteur ; Ronghua Yang, Auteur ; Chang Deng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : 33 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] éclairage
[Termes IGN] intensité lumineuse
[Termes IGN] motif binaire local
[Termes IGN] réalité augmentée
[Termes IGN] scène urbaine
[Termes IGN] SIFT (algorithme)
[Termes IGN] SURF (algorithme)Résumé : (auteur) Numéro de notice : A2020-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060362 Date de publication en ligne : 01/06/2020 En ligne : https://doi.org/10.3390/ijgi9060362 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95166
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 33 p.[article]Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)
[article]
Titre : Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data Type de document : Article/Communication Auteurs : Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; Zhijia Zheng, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] cartographie urbaine
[Termes IGN] Chine
[Termes IGN] échantillonnage d'image
[Termes IGN] image à très haute résolution
[Termes IGN] méthode heuristique
[Termes IGN] point d'intérêt
[Termes IGN] scène urbaineRésumé : (Auteur) Urban functional zones are basic units of urban planning and resource allocation, and contribute to a wide range of urban studies and investigations. Existing studies on functional-zone mapping with very-high-resolution (VHR) satellite images focused much on feature representations and classification techniques, but ignored zone sampling which however was fundamental to automatic zone classifications. Functional-zone sampling is much complicated and can hardly be resolved by classical sampling methods, as functional zones are complex urban scenes which consist of heterogeneous land covers and have highly abstract categories. To resolve the issue, this study presents a novel sampling paradigm, i.e., heuristic sample learning (HSL). It first proposes a sparse topic model to select representative functional zones, then uses deep forest to select confusing zones, and finally embraces Chinese restaurant process to label these selected zones. The presented method collects both representative and confusing zone samples and identifies their categories accurately, which makes the functional-zone classification process robust and the classification results accurate. Experiments conducted in Beijing indicate that HSL is effective and efficient for functional-zone sampling and classifications. Compared to traditional manual sampling, HSL reduces the time cost by 55% and improves the classification accuracy by 11.3% on average; furthermore, HSL can reduce the variation in sampling and classification results caused by different proficiency of operators. Accordingly, HSL significantly contributes to functional-zone mapping and plays an important role in urban studies. Numéro de notice : A2020-061 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.005 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94577
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 1 - 12[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Object-based incremental registration of terrestrial point clouds in an urban environment / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkCartographie sémantique hybride de scènes urbaines à partir de données image et Lidar / Mohamed Boussaha (2020)PermalinkCreation of inspirational Web Apps that demonstrate the functionalities offered by the ArcGIS API for JavaScript / Arthur Genet (2020)PermalinkPermalinkPoint cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning / Ephraim Friedli (2020)PermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)PermalinkSimulation d’éclairements des surfaces ombrées en zone urbaine par transfert radiatif 3D (modèle DART) / Yulu Xi (2020)PermalinkUnderwater calibration in near real time: Focus on detection optimized by AI and selection of calibration patterns / Loïca Avanthey (2020)PermalinkA versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions / Thanh Huy Nguyen (2020)PermalinkIntroducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)Permalink