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Ailanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)
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Titre : Ailanthus altissima mapping from multi-temporal very high resolution satellite images Type de document : Article/Communication Auteurs : Cristina Tarantino, Auteur ; Francesca Casella, Auteur ; Maria Adamo, Auteur ; Richard Lucas, Auteur ; Carl Beierkuhnlein, Auteur ; Palma Blonda, Auteur Année de publication : 2019 Article en page(s) : pp 90 - 103 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Ailanthus altissima
[Termes IGN] analyse diachronique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce exotique envahissante
[Termes IGN] filtrage optique
[Termes IGN] filtre passe-bas
[Termes IGN] image à très haute résolution
[Termes IGN] image multitemporelle
[Termes IGN] image Worldview
[Termes IGN] indice de végétation
[Termes IGN] ItalieRésumé : (auteur) This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ≈91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User’s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision. Numéro de notice : A2019-035 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.013 Date de publication en ligne : 20/11/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91972
in ISPRS Journal of photogrammetry and remote sensing > vol 147 (January 2019) . - pp 90 - 103[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019011 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019013 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2019012 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Archival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues / Arnaud Le Bris (2019)
Titre : Archival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Sébastien Giordano
, Auteur
Editeur : Leibniz : Leibniz Institute of Ecological Urban and Regional Development Année de publication : 2019 Conférence : ILUS 2019, 3rd International land use symposium, Land use changes: Trends and projections 04/12/2019 06/12/2019 Paris France programme sans actes Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] automatisation
[Termes IGN] chaîne de traitement
[Termes IGN] détection de changement
[Termes IGN] égalisation radiométrique
[Termes IGN] géoréférencement indirect
[Termes IGN] image numérisée
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du sol
[Termes IGN] orientation absolue
[Termes IGN] orthoimage
[Termes IGN] orthophotoplan argentique
[Termes IGN] vingtième siècleRésumé : (auteur) Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid20th century. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Thus, they offer time series data which can answer to a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. For instance, IGN (the French national mapping agency) has digitized its archival aerial photogrammetric surveys: images can be downloaded from a web service (http://remonterletemps.ign.fr/) Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. A first bottleneck for accurate comparison between epochs is their fine georeferencing. Such information has generally been lost and must be retrieved. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the-art shows that one major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is often manually performed, and extremely time-consuming. This contribution proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a the use of the coarse absolute image orientation from existing metadata to derive coarse Digital Surface Model (DSM) and orthoimage that are then used to improve absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. A new iteration of the georeferencing process can then be done using these ground references. At the end, images orientation is retrieved and orthoimages as well as digital surface models can be computed for each epoch. Another issue is the radiometric equalization of images (to remove atmospheric as well as vignetting effects), in order to obtain a homogeneous mosaic suitable with automatic classification processes. Last but not least, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, color, infrared...). Planimetric and altimetric accuracies of derived products depend on these characteristics. Thus, automatic change detection and analysis processes have to cope with these uncertainties. Numéro de notice : C2019-069 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97024
Titre : Atlas of remote sensing of the Wenchuan earthquake : Cas- Project Team of Remote Sensing for Wenchuan Earthquake Type de document : Monographie Auteurs : Huadong Guo, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2019 Importance : 259 p. Format : 26 x 28 cm ISBN/ISSN/EAN : 978-1-4398-1674-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse du paysage
[Termes IGN] carte géologique
[Termes IGN] détection de changement
[Termes IGN] dommage matériel
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] image Radarsat
[Termes IGN] image satellite
[Termes IGN] impact sur l'environnement
[Termes IGN] réseau routier
[Termes IGN] séisme
[Termes IGN] Setchouan (Chine)
[Termes IGN] zone urbaineRésumé : (éditeur) In May 12, 2008, the Wenchuan County earthquake caused devastating loss of human life and property. Applying all the remote sensing technology available, the Chinese Academy of Sciences immediately launched into action, making full use of its state-of-the-art facilities, remote sensing planes, and satellites to amass invaluable optical and radar data. This unprecedented use of comprehensive remote sensing techniques provided accurate, up to the minute information for disaster management and has left us with a visually stunning and beautiful record that is as much a scientific achievement as it is an artistic one. Based on the accumulated data and images collected by the Project Team of Remote Sensing Monitoring and Assessment of the Wenchuan Earthquake, Atlas of Remote Sensing of the Wenchuan Earthquake documents the events as they happened in real time. The book covers the disaster from six aspects: geological, barrier lakes, collapsed buildings, damaged roads, destroyed farmland and forests, and demolished infrastructure. It also demonstrates that the Dujiangyan Irrigation Project, which has been standing for 2000 years, remains fully functioning, and keeps the Chengdu Plain operating optimally even after the earthquake. Translated into English for the first time, the Atlas presents a pictorial summation of this unique project. It chronicles the event with over 280 before and after color images from a range of perspectives. This volume dramatically demonstrates the value of remote sensing for understanding how an earthquake unfolds and the potential of remote sensing in helping coordinate emergency relief. A pictorial record of events as they unfolded, this book provides a systematic documentation of earthquake damage that can be used to prepare for future seismic events. Note de contenu : 1- Remote sensing data
2- Geological disaster
3- Barrier lakes
4- Collapsed buildings
5- Damaged roads
6- Destroyed farmlands and forests
7- Demolished infrastructure
8- Civilization perseveresNuméro de notice : 25917 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie En ligne : https://library.oapen.org/handle/20.500.12657/40124 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96100 Challenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)
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Titre : Challenges in grassland mowing event detection with multimodal Sentinel images Type de document : Article/Communication Auteurs : Anatol Garioud , Auteur ; Sébastien Giordano
, Auteur ; Silvia Valero, Auteur ; Clément Mallet
, Auteur
Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2019 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : MultiTemp 2019, 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images 05/08/2019 07/08/2019 Shanghai Chine Proceedings IEEE Importance : pp 1 - 4 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] détection d'événement
[Termes IGN] données lidar
[Termes IGN] image multibande
[Termes IGN] image RapidEye
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] image TerraSAR-X
[Termes IGN] méthode robuste
[Termes IGN] nébulosité
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Perceptron multicouche
[Termes IGN] prairie
[Termes IGN] régression
[Termes IGN] réseau neuronal récurrent
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétationRésumé : (auteur) Permanent Grasslands (PG) are heterogeneous environments with high spatial and temporal dynamics, subject to increasing environmental challenges. This study aims to identify requirements, key constraining factors and solutions for robust and complete detection of Mowing Events. Remote sensing is a powerful tool to monitor and investigate Near-Real-Time and seasonally PG cover. Here, pros and cons of Sentinel-2 (S2) and Sentinel-1 (S1) time series exploitation for Mowing Events (MowEve) detection are analysed. A deep-based approach is proposed to obtain consistent and homogeneous biophysical parameter times series for MowEve detection. Recurrent Neural Networks are proposed as regression strategy allowing the synergistic integration of optical and Synthetic Aperture Radar data to reconstruct dense NDVI times series. Experimental results corroborates the interest of deriving consistent and homogeneous series of biophysical parameters for subsequent MowEve detection. Numéro de notice : C2019-028 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/Multi-Temp.2019.8866914 Date de publication en ligne : 29/11/2019 En ligne : https://doi.org/10.1109/Multi-Temp.2019.8866914 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94538 Détection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)
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Titre : Détection et localisation d'objets 3D par apprentissage profond en topologie capteur Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Aurélie Bugeau, Auteur ; Jean-François Aujol, Auteur ; Mathieu Brédif
, Auteur
Editeur : Saint-Martin-d'Hères : Groupe de Recherche et d'Etude du Traitement du Signal et des Images GRETSI Année de publication : 2019 Projets : 1-Pas de projet / Conférence : GRETSI 2019, colloque du Groupe de Recherche et d'Etude du Traitement du Signal et des Images 26/08/2019 29/08/2019 Lille France OA proceedings Importance : 4 p. Format : 21 x 30 cm Note générale : Bibliographie
Ce travail a bénéficié d’une aide du programme de Recherche et Innovation European Union’s Horizon 2020 au titre de la bourse Marie Skłodowska-Curie (No 777826).Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] fusion de données
[Termes IGN] image optique
[Termes IGN] scène 3D
[Termes IGN] semis de pointsRésumé : (Auteur) Ce travail présente une nouvelle méthode pour la détection et la localisation d'objets dans des scènes 3D LiDAR acquises par des systèmes de cartographie mobile. Ce problème est généralement traité en discrétisant l'espace 3D en une fine grille de voxels. Nous introduisons une approche alternative ne nécessitant pas de discrétisation. Elle est basée sur la représentation en 2D du nuage de points en topologie capteur (TC). Cette image sert d'entrée à un réseau de neurones convolutionnels qui en extrait les informations 3D des objets. La réprésentation en topologie capteur présentant des ambiguïtés dans le fond de la scène, nous améliorerons les résultats de détection en couplant ce modèle avec un réseau de détection 2D d'objets sur une image optique. Les prédictions des deux réseaux sont finalement fusionnées pour obtenir les détections finales. Numéro de notice : C2019-014 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Poster nature-HAL : Poster-avec-CL DOI : sans En ligne : https://hal.science/hal-02100719v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93269 Documents numériques
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