Descripteur
Termes IGN > imagerie > image aérienne > image à ultra haute résolution
image à ultra haute résolution |
Documents disponibles dans cette catégorie (22)



Etendre la recherche sur niveau(x) vers le bas
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)
![]()
[article]
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 IGN] apprentissage profond
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] carte d'occupation du sol
[Termes IGN] enrichissement sémantique
[Termes IGN] filtrage numérique d'image
[Termes IGN] image à ultra haute résolution
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (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 Affiliation des auteurs : non IGN 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
in ISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018)[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018113 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018112 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Aerial data acquisition for a digital railway / James Dunthorne in GIM international, vol 32 n° 4 (July - August 2018)
![]()
[article]
Titre : Aerial data acquisition for a digital railway Type de document : Article/Communication Auteurs : James Dunthorne, Auteur Année de publication : 2018 Article en page(s) : pp 26 - 27 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] acquisition de données
[Termes IGN] chambre à moyen format
[Termes IGN] image à ultra haute résolution
[Termes IGN] image captée par drone
[Termes IGN] logiciel de visualisation
[Termes IGN] réseau ferroviaire
[Termes IGN] Royaume-UniRésumé : (auteur) One of the major challenges facing railway networks is preventing failures in railway tracks. Avoiding potential track malfunctions means inspecting thousands of miles of track, while avoiding risk to inspectors and traffic interference. One innovative inspection methodology is to build a ‘digital railway’ – an accurate and dynamic visualisation tool to identify actual and potential track damage. Relying on the highest-quality data acquisition, a digital railway helps those responsible to make better informed decisions while planning and prioritising rail development, maintenance, repairs and renewal projects. This article outlines the use of such a tool in a UK railway project. Numéro de notice : A2018-243 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://www.gim-international.com/content/article/aerial-data-acquisition-for-a- [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90303
in GIM international > vol 32 n° 4 (July - August 2018) . - pp 26 - 27[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 061-2018041 RAB Revue Centre de documentation En réserve L003 Disponible An iterative interpolation deconvolution algorithm for superresolution land cover mapping / Feng Ling in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)
![]()
[article]
Titre : An iterative interpolation deconvolution algorithm for superresolution land cover mapping Type de document : Article/Communication Auteurs : Feng Ling, Auteur ; Giles M. Foody, Auteur ; Yong Ge, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7210 - 7222 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification du maximum a posteriori
[Termes IGN] déconvolution
[Termes IGN] image à ultra haute résolution
[Termes IGN] itérationRésumé : (Auteur) Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms. Numéro de notice : A2016-928 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598534 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83342
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7210 - 7222[article]Automatic segment-level tree species recognition using high resolution aerial winter imagery / Anton Kuzmin in European journal of remote sensing, vol 49 n° 1 (2016)
![]()
[article]
Titre : Automatic segment-level tree species recognition using high resolution aerial winter imagery Type de document : Article/Communication Auteurs : Anton Kuzmin, Auteur ; Lauri Korhonen, Auteur ; Terhikki Manninen, Auteur ; Matti Maltamo, Auteur Année de publication : 2016 Article en page(s) : pp 239 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse discriminante
[Termes IGN] betula pubescens
[Termes IGN] composition floristique
[Termes IGN] forêt boréale
[Termes IGN] hélicoptère
[Termes IGN] hiver
[Termes IGN] image à ultra haute résolution
[Termes IGN] image aérienne
[Termes IGN] neige
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestrisRésumé : (auteur) Our objective was to automatically recognize the species composition of a boreal forest from high-resolution airborne winter imagery. The forest floor was covered by snow so that the contrast between the crowns and the background was maximized. The images were taken from a helicopter flying at low altitude so that fine details of the canopy structure could be distinguished. Segments created by an object-oriented image processing were used as a basis for a linear discriminant analysis, which aimed at separating the three dominant tree species occurring in the area: Scots pine, Norway spruce, and downy birch. In a cross validation, the classification showed an overall accuracy of 81.9%, and a kappa coefficient of 0.73. Numéro de notice : A2016-831 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164914 En ligne : http://dx.doi.org/10.5721/EuJRS20164914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82714
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 239 - 259[article]Revealing a buried historic fort : archeology meets UAS technology / Andrea Sangster in Geoinformatics, vol 18 n° 7 (October - November 2015)
[article]
Titre : Revealing a buried historic fort : archeology meets UAS technology Type de document : Article/Communication Auteurs : Andrea Sangster, Auteur Année de publication : 2015 Article en page(s) : pp 18 - 19 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] Canada
[Termes IGN] drone
[Termes IGN] fortification
[Termes IGN] image à ultra haute résolution
[Termes IGN] précision centimétriqueNuméro de notice : A2015-781 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78879
in Geoinformatics > vol 18 n° 7 (October - November 2015) . - pp 18 - 19[article]Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkTraining set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkData-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)
PermalinkAdaptive subpixel mapping based on a multiagent system for remote-sensing imagery / Xiong Xu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkMultiagent object-based classifier for high spatial resolution imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
PermalinkHierarchical extraction of landslides from multiresolution remotely sensed optical images / Camille Kurtz in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
PermalinkSuperresolution multitarget parameter estimation in MIMO radar / Kai Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 6 Tome 2 (June 2013)
PermalinkMapping impervious surfaces from superresolution enhanced CHRIS/Proba imagery using multiple endmember unmixing / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 72 (August 2012)
PermalinkPermalinkPermalink