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imagerie
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Terme regroupant photographies et images issues de différents capteurs.
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Semantic labeling in very high resolution images via a self-cascaded convolutional neural network / Yoncheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)
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Titre : Semantic labeling in very high resolution images via a self-cascaded convolutional neural network Type de document : Article/Communication Auteurs : Yoncheng Liu, Auteur ; Bin Fan, Auteur ; Lingfeng Wang, Auteur ; Jun Bai, Auteur ; Shiming Xiang, Auteur ; Chunhong Pan, Auteur Année de publication : 2018 Article en page(s) : pp 78 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] image à très haute résolution
[Termes IGN] réseau neuronal convolutif
[Termes IGN] zone urbaineRésumé : (Auteur) Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN’s shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance. Numéro de notice : A2018-490 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.007 Date de publication en ligne : 21/12/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91226
in ISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 78 - 95[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 A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification / Wei Han in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)
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Titre : A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification Type de document : Article/Communication Auteurs : Wei Han, Auteur ; Ruyi Feng, Auteur ; Lizhe Wang, Auteur ; Yafan Cheng, Auteur Année de publication : 2018 Article en page(s) : pp 23 - 43 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de sensibilité
[Termes IGN] apprentissage profond
[Termes IGN] classification semi-dirigée
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scèneRésumé : (Auteur) High resolution remote sensing (HRRS) image scene classification plays a crucial role in a wide range of applications and has been receiving significant attention. Recently, remarkable efforts have been made to develop a variety of approaches for HRRS scene classification, wherein deep-learning-based methods have achieved considerable performance in comparison with state-of-the-art methods. However, the deep-learning-based methods have faced a severe limitation that a great number of manually-annotated HRRS samples are needed to obtain a reliable model. However, there are still not sufficient annotation datasets in the field of remote sensing. In addition, it is a challenge to get a large scale HRRS image dataset due to the abundant diversities and variations in HRRS images. In order to address the problem, we propose a semi-supervised generative framework (SSGF), which combines the deep learning features, a self-label technique, and a discriminative evaluation method to complete the task of scene classification and annotating datasets. On this basis, we further develop an extended algorithm (SSGA-E) and evaluate it by exclusive experiments. The experimental results show that the SSGA-E outperforms most of the fully-supervised methods and semi-supervised methods. It has achieved the third best accuracy on the UCM dataset, the second best accuracy on the WHU-RS, the NWPU-RESISC45, and the AID datasets. The impressive results demonstrate that the proposed SSGF and the extended method is effective to solve the problem of lacking an annotated HRRS dataset, which can learn valuable information from unlabeled samples to improve classification ability and obtain a reliable annotation dataset for supervised learning. Numéro de notice : A2018-489 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.11.004 Date de publication en ligne : 14/11/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.11.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91225
in ISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 23 - 43[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 A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery / Zewei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
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Titre : A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery Type de document : Article/Communication Auteurs : Zewei Xu, Auteur ; Kaiyu Guan, Auteur ; Nathan Casler, Auteur ; Bin Peng, Auteur ; Shaowen Wang, Auteur Année de publication : 2018 Article en page(s) : pp 423 - 434 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Illinois (Etats-Unis)
[Termes IGN] image Landsat
[Termes IGN] image multitemporelle
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Terrestrial landscape has complex three-dimensional (3D) features that are difficult to extract using traditional methods based on 2D representations. These methods often relegate such features to raster or metric-based (two-dimensional) representations based on Digital Surface Models (DSM) or Digital Elevation Models (DEM), and thus are not suitable for resolving morphological and intensity features for fine-scale land cover mapping. Small-footprint LiDAR provides an ideal way for capturing these 3D features. This research develops a novel method of integrating airborne LiDAR derived features and multi-temporal Landsat images to classify land cover types. We tested our approach in Williamson County, Illinois, which has diverse and mixed landscape features. Specifically, our method applied a 3D convolutional neural network (CNN) approach to extract features from LiDAR point clouds by (1) creating an occupancy grid, an intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into the 3D CNN. The extracted features (e.g., morphological and intensity features) from the 3D CNN were finally combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. Visual interpretation from both hyper-resolution photos and point clouds was used for training and preparation of testing data. The classification results show that our method outperforms a traditional method by 2.65% (from 81.52% to 84.17%) when solely using LiDAR and 2.19% (from 90.20% to 92.57%) when combining all available imageries. We demonstrate that our method can effectively extract LiDAR features and improve fine-scale land cover mapping through fusion of complementary types of remote sensing data. Numéro de notice : A2018-405 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.08.005 Date de publication en ligne : 22/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90859
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 423 - 434[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Cartographie des forêts humides dans la région d’El Kala (Algérie) à l’aide des outils d’observation de la Terre / Asma Kahli in Revue d'écologie, vol 73 n° 4 (octobre - décembre 2018)
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Titre : Cartographie des forêts humides dans la région d’El Kala (Algérie) à l’aide des outils d’observation de la Terre Type de document : Article/Communication Auteurs : Asma Kahli, Auteur ; Ghania Belhadj, Auteur ; Elie Gaget, Auteur ; Clément Merle, Auteur ; Anis Guelmami, Auteur Année de publication : 2018 Article en page(s) : pp 431 - 445 Note générale : bibliographie Langues : Français (fre) Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Algérie
[Termes IGN] carte forestière
[Termes IGN] distribution spatiale
[Termes IGN] données de terrain
[Termes IGN] forêt
[Termes IGN] image Landsat-8
[Termes IGN] modèle numérique de terrain
[Termes IGN] zone humideRésumé : (auteur) Les forêts humides sont parmi les écosystèmes humides les plus dégradés et les plus menacés dans le monde. En Algérie, elles représentent un ensemble d’habitats forestiers singuliers, fragiles et rares. La région d’El Kala, à l’extrême nord-est du pays, abrite de nombreuses zones humides uniques (lacs, marais, prairies humides, lagunes, etc.), parmi lesquelles quelques-unes des plus importantes formations de forêts humides en Afrique du Nord. L’objectif de cette étude est de développer une nouvelle approche cartographique afin de localiser et de délimiter ces formations à l’aide des outils d’observation de la Terre. Elle se base sur une combinaison d’indices topographiques et hydro-géomorphologiques, issus des Modèles Numériques de Terrain (MNT), de variables spectrales calculées à partir des images Landsat-8 et de données collectées sur terrain. Le résultat final a permis de mettre en évidence l’existence de plus de 3900 ha de forêts humides (aulnaies plus ripisylves) sur l’ensemble des bassins versants de la région d’El Kala, avec un niveau de fiabilité, estimé à partir d’observations terrain, supérieur à 85 %. Ainsi, la méthodologie développée ici permet de définir la distribution spatiale des forêts humides sur de grandes échelles territoriales, ce qui pourrait grandement faciliter leur suivi diachronique, avec des analyses rétrospectives rendues possible grâce aux outils de télédétection, mais aussi une meilleure implémentation des outils dédiés à leur gestion et à leur conservation. Numéro de notice : A2018-603 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : sans En ligne : https://www.persee.fr/doc/revec_0249-7395_2018_num_73_4_1948 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93451
in Revue d'écologie > vol 73 n° 4 (octobre - décembre 2018) . - pp 431 - 445[article]Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
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Titre : Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images Type de document : Article/Communication Auteurs : Michele Volpi, Auteur ; Devis Tuia, Auteur Année de publication : 2018 Article en page(s) : pp 48 - 60 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e.g. convolutional neural networks) with strategies for spatial regularization (e.g. graphical models such as conditional random fields). In this paper, we propose a method to learn evidence in the form of semantic class likelihoods, semantic boundaries across classes and shallow-to-deep visual features, each one modeled by a multi-task convolutional neural network architecture. We combine this bottom-up information with top-down spatial regularization encoded by a conditional random field model optimizing the label space across a hierarchy of segments with constraints related to structural, spatial and data-dependent pairwise relationships between regions. Our results show that such strategy provide better regularization than a series of strong baselines reflecting state-of-the-art technologies. The proposed strategy offers a flexible and principled framework to include several sources of visual and structural information, while allowing for different degrees of spatial regularization accounting for priors about the expected output structures. Numéro de notice : A2018-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.007 Date de publication en ligne : 05/07/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90826
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 48 - 60[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest / Qingxia Zhao in Forests, vol 9 n° 10 (October 2018)
PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)
PermalinkHow to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes / Niko Kulha in Forests, vol 9 n° 10 (October 2018)
PermalinkMulti‐scale observations of atmospheric moisture variability in relation to heavy precipitating systems in the northwestern Mediterranean during HyMeX IOP12 / Samiro Khodayar in Quarterly Journal of the Royal Meteorological Society, vol 144 n° 717 (October 2018 Part B)
PermalinkNovel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)
PermalinkObject-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)
PermalinkPrecise DEM extraction from Svalbard using 1936 high oblique imagery / Luc Girod in Geoscientific instrumentation methods and data systems, vol 7 n° 4 ([01/10/2018])
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PermalinkPredicting tree diameter distributions from airborne laser scanning, SPOT 5 satellite, and field sample data in the perm region, Russia / Jussi Peuhkurinen in Forests, vol 9 n° 10 (October 2018)
PermalinkStand age estimation of rubber (Hevea brasiliensis) plantations using an integrated pixel- and object-based tree growth model and annual Landsat time series / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
PermalinkThe design and testing of 3DmoveR: an experimental tool for usability studies of interactive 3D maps / Lukas Herman in Cartographic perspectives, n° 90 ([01/10/2018])
PermalinkUnmixing polarimetric radar images based on land cover type identified by higher resolution optical data before target decomposition: application to forest and bare soil / Sébastien Giordano in IEEE Transactions on geoscience and remote sensing, vol 56 n° 10 (October 2018)
PermalinkAncient Chinese architecture 3D preservation by merging ground and aerial point clouds / Xiang Gao in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)
PermalinkAssessment of Nigeriasat-1 satellite data for urban land use/land cover analysis using object-based image analysis in Abuja, Nigeria / Christopher Ifechukwude Chima in Geocarto international, vol 33 n° 9 (September 2018)
PermalinkAugmented reality meets computer vision : efficient data generation for urban driving scenes / Hassan Abu Alhaija in International journal of computer vision, vol 126 n° 9 (September 2018)
PermalinkLa cartographie mobile et le géoréférencement précis de réseaux souterrains / Garance Weller in XYZ, n° 156 (septembre - novembre 2018)
PermalinkConfigurable 3D scene synthesis and 2D image rendering with per-pixel ground truth using stochastic grammars / Chenfanfu Jiang in International journal of computer vision, vol 126 n° 9 (September 2018)
PermalinkEffects of a large-scale late spring frost on a beech (Fagus sylvatica L.) dominated Mediterranean mountain forest derived from the spatio-temporal variations of NDVI / Angelo Nolè in Annals of Forest Science, vol 75 n° 3 (September 2018)
PermalinkEstimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)
PermalinkFusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)
PermalinkImprovement of countrywide vegetation mapping over Japan and comparison to existing maps / Ram C. Sharma in Advances in Remote Sensing, vol 7 n° 3 (September 2018)
PermalinkIntegration of ZY3-02 satellite laser altimetry data and stereo images for high-accuracy mapping / Guoyuan Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 9 (September 2018)
PermalinkInvestigation of the success of monitoring slow motion landslides using Persistent Scatterer Interferometry and GNSS methods / K.O. Hastaoglu in Survey review, vol 50 n° 363 (September 2018)
PermalinkSynergetic use of Sentinel-1 and Sentinel-2 for assessments of heathland conservation status / Johannes Schmidt in Remote sensing in ecology and conservation, vol 4 n° 3 (September 2018)
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PermalinkThe 2015 Mw 6.4 Pishan earthquake, China: geodetic modelling inferred from Sentinel-1A TOPS interferometry / Yongsheng Li in Survey review, vol 50 n° 363 (September 2018)
Permalink3-D deep learning approach for remote sensing image classification / Amina Ben Hamida in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)
PermalinkAn improved temporal mixture analysis unmixing method for estimating impervious surface area based on MODIS and DMSP-OLS data / Li Zhuo in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkComparison of high-density LiDAR and satellite photogrammetry for forest inventory / Grant D. Pearse in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkA deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)
PermalinkDetecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques / Chinsu Lin in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkEstimating storm damage with the help of low-altitude photographs and different sampling designs and estimators / Pekka Hyvönen in Silva fennica, vol 52 n° 3 ([01/08/2018])
PermalinkA generic remote sensing approach to derive operational essential biodiversity variables (EBVs) for conservation planning / Samuel Alleaume in Methods in ecology and evolution, vol 9 n° 8 (August 2018)
PermalinkICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkIncorporating crown shape information for identifying ash tree species / Haijian Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 8 (août 2018)
PermalinkIntra-annual phenology for detecting understory plant invasion in urban forests / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 142 (August 2018)
PermalinkRobust detection and affine rectification of planar homogeneous texture for scene understanding / Shahzor Ahmad in International journal of computer vision, vol 126 n° 8 (August 2018)
PermalinkSpectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields / Elham Kordi Ghasrodashti in Geocarto international, vol 33 n° 8 (August 2018)
PermalinkAerial data acquisition for a digital railway / James Dunthorne in GIM international, vol 32 n° 4 (July - August 2018)
PermalinkPermalinkPermalinkEvolutionary approach for detection of buried remains using hyperspectral images / Leon Dozal in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 7 (juillet 2018)
PermalinkExploring geo-tagged photos for land cover validation with deep learning / Hanfa Xing in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
PermalinkExtracting leaf area index using viewing geometry effects : A new perspective on high-resolution unmanned aerial system photography / Lukas Roth in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
PermalinkA fully automatic approach to register mobile mapping and airborne imagery to support the correction of plateform trajectories in GNSS-denied urban areas / Phillipp Jende in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
PermalinkA light and faster regional convolutional neural network for object detection in optical remote sensing images / Peng Ding in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
PermalinkMapping ecosystem services at the regional scale: the validity of an upscaling approach / Solen Le Clec'h in International journal of geographical information science IJGIS, vol 32 n° 7-8 (July - August 2018)
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