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Urban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)
Titre : Urban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : GeoSud / Conférence : JURSE 2019, Joint Urban Remote Sensing Event 22/05/2019 24/05/2019 Vannes France Proceedings IEEE Importance : 4 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] morphologie urbaine
[Termes IGN] série temporelle
[Termes IGN] zone urbaineRésumé : (auteur) This paper aims at detecting several urban morpho-type classes out of SPOT-6/7 imagery and Sentinel-2 time series. Urban classes of Urban Atlas are considered. The proposed strategy is a bottom-up one. It first detects basic urban objects (buildings, roads, vegetation), and use them to calculate multi-scale morphological features. These features are then fed to a Random Forest classifier trained from samples out of Urban Atlas urban classes. Obtained results is optionally merged with a Random Forest classification based on Sentinel-2 time series. Obtained results are promising. Numéro de notice : C2019-004 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/URBANISME Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2019.8808988 Date de publication en ligne : 22/08/2019 En ligne : https://doi.org/10.1109/JURSE.2019.8808988 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92209 Utilisation de données Sentinel-2 et SPOT 6/7 pour la classification de l’occupation du sol / Olivier Stocker (2019)
Titre : Utilisation de données Sentinel-2 et SPOT 6/7 pour la classification de l’occupation du sol Type de document : Mémoire Auteurs : Olivier Stocker, Auteur ; Arnaud Le Bris , Encadrant Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2019 Importance : 70 p. Note générale : bibliographie
Rapport de stage Mastère spécialisé Photogrammétrie, Positionnement, Mesure de DéformationsLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Cette étude porte sur le développement d’une architecture entièrement convolutive, adaptée au traitement de l’information spatiale apportée par la très haute résolution des capteurs SPOT 6 et 7. Cette architecture s’est montrée plus performante que les approches par fenêtre glissante dans la précision de la détection des objets topographiques, même en zone dense. Parallèlement, ces travaux montrent que l’ajout de contraintes permet de mieux délimiter les objets et que la finesse de la vérité terrain joue un grand rôle dans cette capacité de délimitation. Cette nouvelle architecture a également permis de générer, à partir de produits existants, des cartes de couverture du sol d’une qualité prometteuse. Les différents niveaux de richesse de nomenclatures évalués ont mis en avant une capacité de constance dans la segmentation sémantique. Enfin, ces travaux ont servi d’étude préliminaire à la fusion tardive et précoce des données SPOT 6/7 et Sentinel 2, dans l’objectif d’ajouter à la richesse spatiale, déjà efficace, une dimension spectrale. L’ensemble des contraintes liées à l’implantation entièrement convolutive de la fusion et les modifications à appliquer sur notre architecture ont été listées. Note de contenu : Introduction
1- Classification de l'occupation du sol
2- Données et traitement
3- Algorithmique
4- Segmentation sémantique entièrement convolutive
5- Segmentation par fusion
ConclusionNuméro de notice : 17344 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Mémoire PPMD Organisme de stage : LaSTIG (IGN) DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98316 Documents numériques
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Utilisation de données Sentinel-2 et SPOT 6/7 ... - pdf auteurAdobe Acrobat PDF Variational learning of mixture wishart model for PolSAR image classification / Qian Wu in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)
[article]
Titre : Variational learning of mixture wishart model for PolSAR image classification Type de document : Article/Communication Auteurs : Qian Wu, Auteur ; Biao Hou, Auteur ; Zaidao Wen, Auteur ; Licheng Jiao, Auteur Année de publication : 2019 Article en page(s) : pp 141 - 154 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification
[Termes IGN] image AIRSAR
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] loi de Wishart
[Termes IGN] optimisation (mathématiques)
[Termes IGN] polarimétrie radarRésumé : (Auteur) The phase difference, amplitude product, and amplitude ratio between two polarizations are important discriminators for terrain classification, which derives a significant statistical-distribution-based polarimetric synthetic aperture radar (PolSAR) image classification. Traditionally, statistical-distribution-based PolSAR image classification models pay attention to two aspects: searching for a suitable distribution to model certain PolSAR image and a satisfactory solution for the corresponding distribution model with samples in every terrain. Usually, the described distribution form is too complicated to build. Besides, inaccurate parameter estimation may lead to poor classification performance for PolSAR image. In order to refrain from this phenomenon, a variational thought is adopted for the statistical-distribution-based PolSAR classification method in this paper. First, a mixture Wishart model is built to model the PolSAR image to replace the complicated distribution for the PolSAR image. Second, a learning-based method is suggested instead of inaccurate point estimation of parameters to determine the distribution for every class in the mixture Wishart model. Finally, the proposed learning-based mixture Wishart model will be built as a variational form to realize a parametric model for PolSAR image classification. In the experiments, it will be proved that the class centers are easier to distinguish among different terrains learned from the proposed variational model. In addition, a classification performance on the PolSAR image is superior to the original point estimation Wishart model on both visual classification result and accuracy. Numéro de notice : A2019-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2852633 Date de publication en ligne : 16/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2852633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92410
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 141 - 154[article]
Titre : Very High Resolution (VHR) satellite imagery : processing and applications Type de document : Monographie Auteurs : Francisco Eugenio, Éditeur scientifique ; Javier Marcello, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 262 p. ISBN/ISSN/EAN : 978-3-03921-757-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse texturale
[Termes IGN] détection du bâti
[Termes IGN] forêt alpestre
[Termes IGN] image à très haute résolution
[Termes IGN] image Pléiades
[Termes IGN] image Quickbird
[Termes IGN] image Worldview
[Termes IGN] risque naturel
[Termes IGN] traitement d'imageRésumé : (Editeur) Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing. Numéro de notice : 26310 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique DOI : 10.3390/books978-3-03921-757-1 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.3390/books978-3-03921-757-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95071 Atmospheric artifacts correction with a covariance-weighted linear model over mountainous regions / Zhongbo Hu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)
[article]
Titre : Atmospheric artifacts correction with a covariance-weighted linear model over mountainous regions Type de document : Article/Communication Auteurs : Zhongbo Hu, Auteur ; Hongdong Fan, Auteur ; Jordi J. Mallorquí, Auteur Année de publication : 2018 Article en page(s) : pp 6995 - 70008 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] correction atmosphérique
[Termes IGN] image Sentinel-SAR
[Termes IGN] interferométrie différentielle
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] matrice de covariance
[Termes IGN] modèle linéaire
[Termes IGN] montagne
[Termes IGN] retard troposphérique
[Termes IGN] Tenerife
[Termes IGN] variogrammeRésumé : (auteur) Mitigating the atmospheric phase delay is one of the largest challenges faced by the differential synthetic aperture radar (SAR) interferometry community. Recently, many publications have studied correcting the stratified tropospheric phase delay by assuming a linear model between them and the topography. However, most of these studies have not considered the effect of turbulent atmospheric artifacts when adjusting the linear model to data. In this paper, we present an improved technique that minimizes the influence of the turbulent atmosphere in the model adjustment. In the proposed algorithm, the model is adjusted to the phase differences of pixels instead of using the unwrapped phase of each pixel. In addition, the different phase differences are weighted as a function of its atmospheric phase screen covariance estimated from an empirical variogram to reduce, in the model adjustment, the impact of pixel pairs with a significant turbulent atmosphere. The good performance of the proposed method has been validated with both the simulated and real Sentinel-1A SAR data in the mountainous area of Tenerife island, Spain. Numéro de notice : A2018- 553 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2846885 Date de publication en ligne : 17/07/2018 En ligne : http://dx.doi.org/ 10.1109/TGRS.2018.2846885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91652
in IEEE Transactions on geoscience and remote sensing > vol 56 n° 12 (December 2018) . - pp 6995 - 70008[article]Automatic building rooftop extraction from aerial images via hierarchical RGB-D priors / Shibiao Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkLong-term land deformation monitoring using quasi-persistent scatterer (Q-PS) technique observed by sentinel-1A : case study Kelok Sembilan / Pakhrur Razi in Advances in Remote Sensing, vol 7 n° 4 (December 2018)PermalinkA new generation of the United States National Land Cover Database : Requirements, research priorities, design, and implementation strategies / Limin Yang in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkPolarimetric radar vegetation index for biomass estimation in desert fringe ecosystems / Jisung Geba Chang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPotential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)PermalinkScene classification based on multiscale convolutional neural network / Yanfei Liu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkSuper-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)PermalinkUrban impervious surface estimation from remote sensing and social data / Yan Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 12 (December 2018)PermalinkApplication of Landsat-8 and ASTER satellite remote sensing data for porphyry copper exploration: a case study from Shahr-e-Babak, Kerman, south of Iran / Morteza Safari in Geocarto international, vol 33 n° 11 (November 2018)PermalinkChange detection based on stacked generalization system with segmentation constraint / Kun Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 11 (November 2018)PermalinkIndividual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images / Fabien Hubert Wagner in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part B (November 2018)PermalinkMulti-scale object detection in remote sensing imagery with convolutional neural networks / Zhipeng Deng in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkPan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)PermalinkSemantic 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)PermalinkA 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)PermalinkCartographie 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)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)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)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)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)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)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)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)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)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 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)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)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)PermalinkA method of downscaling temperature maps based on analytical hillshading for use in species distribution modelling / Ángel M. Felicísimo in Cartography and Geographic Information Science, Vol 45 n° 4 (July 2018)PermalinkMulti-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data / Siddhartha Khare in Geocarto international, vol 33 n° 7 (July 2018)PermalinkSoil moisture estimation in Ferlo region (Senegal) using radar (ENVISAT/ASAR) and optical (SPOT/VEGETATION) data / Gayane Faye in The Egyptian Journal of Remote Sensing and Space Science, Vol. 21 suppl.1 (juillet 2018)PermalinkAssessment of Sentinel-1A data for rice crop classification using random forests and support vector machines / Nguyen-Thanh Son in Geocarto international, vol 33 n° 6 (June 2018)PermalinkClassification à très large échelle d’images satellites à très haute résolution spatiale par réseaux de neurones convolutifs / Tristan Postadjian in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)PermalinkMapping rubber trees based on phenological analysis of Landsat time series data-sets / Janatul Aziera binti Abd Razak in Geocarto international, vol 33 n° 6 (June 2018)PermalinkModeling of inland flood vulnerability zones through remote sensing and GIS techniques in the highland region of Papua New Guinea / Porejane Harley in Applied geomatics, vol 10 n° 2 (June 2018)Permalink