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A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations / Laure Chandelier in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)
Titre : A worldwide 3D GCP database inherited from 20 years of massive multi-satellite observations Type de document : Article/Communication Auteurs : Laure Chandelier Congrès : Congrès: ISPRS 2020, virtual Congress Imaging today foreseeing tomorrow (31 aout - 2 septembre 2020; Nice, France), Commanditaire Année de publication : 2020 Projets : 1-Pas de projet / Article en page(s) : pp 15 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] compensation par bloc
[Termes descripteurs IGN] données localisées de référence
[Termes descripteurs IGN] formatage
[Termes descripteurs IGN] image à très haute résolution
[Termes descripteurs IGN] image multi sources
[Termes descripteurs IGN] image satellite
[Termes descripteurs IGN] image SPOT 6
[Termes descripteurs IGN] image SPOT 7
[Termes descripteurs IGN] image SPOT-HRS
[Termes descripteurs IGN] informatique en nuage
[Termes descripteurs IGN] Institut national de l'information géographique et forestière (France)
[Termes descripteurs IGN] point d'appui
[Termes descripteurs IGN] spatiotriangulationRésumé : (auteur) High location accuracy is a major requirement for satellite image users. Target performance is usually achieved thanks to either specific on-board satellite equipment or an auxiliary registration reference dataset. Both methods may be expensive and with certain limitations in terms of performance. The Institut national de l’information géographique et forestière (IGN) and Airbus Defence and Space (ADS) have worked together for almost 20 years, to build reference data for improving image location using multi-satellite observations. The first geometric foundation created has mainly used SPOT 5 High Resolution Stereoscopic (HRS) imagery, ancillary Ground Control Points (GCP) and Very High Resolution (VHR) imagery, providing a homogenous location accuracy of 10m CE90 almost all over the world in 2010. Space Reference Points (SRP) is a new worldwide 3D GCP database, built from a plethoric SPOT 6/7 multi-view archive, largely automatically processed, with cloud-based technologies. SRP aims at providing a systematic and reliable solution for image location (Unmanned Aerial Vehicle, VHR satellite imagery, High Altitudes Pseudo-Satellite…) and similar topics thanks to a high-density point distribution with a 3m CE90 accuracy. This paper describes the principle of SRP generation and presents the first validation results. A SPOT 6/7 smart image selection is performed to keep only relevant images for SRP purpose. The location of these SPOT 6/7 images is refined thanks to a spatiotriangulation on the worldwide geometric foundation, itself improved where needed. Points making up the future SRP database are afterward extracted thanks to classical feature detection algorithms and with respect to the expected density. Different filtering methods are applied to keep the best candidates. The last step of the processing chain is the formatting of the data to the delivery format, including metadata. An example of validation of SRP concept and specification on two tests sites (Spain and China) is then given. As a conclusion, the on-going production is shortly presented. Numéro de notice : A2020-474 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-15-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-15-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95613
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 15 - 23[article]
Titre : Indoor positioning using PnP problem on mobile phone images Type de document : Article/Communication Auteurs : Hana Kubickova, Auteur ; Karel Jedlička, Auteur ; Radek Fiala, Auteur ; Daniel Beran, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] appariement d'images
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] décomposition d'image
[Termes descripteurs IGN] extraction de traits caractéristiques
[Termes descripteurs IGN] géométrie épipolaire
[Termes descripteurs IGN] GNSS-INS
[Termes descripteurs IGN] point d'appui
[Termes descripteurs IGN] positionnement en intérieur
[Termes descripteurs IGN] recherche d'image basée sur le contenu
[Termes descripteurs IGN] SIFT (algorithme)
[Termes descripteurs IGN] téléphone intelligent
[Termes descripteurs IGN] vision par ordinateurRésumé : (auteur) As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management. Numéro de notice : A2020-309 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060368 date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.3390/ijgi9060368 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95156
in ISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 19 p.[article]
Titre : Automated terrain feature identification from remote sensing imagery: a deep learning approach Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Chia-Yu Hsu, Auteur Année de publication : 2020 Article en page(s) : pp 637 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes descripteurs IGN] analyse d'image orientée objet
[Termes descripteurs IGN] analyse du paysage
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] classification par réseau neuronal convolutif
[Termes descripteurs IGN] compréhension de l'image
[Termes descripteurs IGN] détection automatique
[Termes descripteurs IGN] détection d'objet
[Termes descripteurs IGN] extraction de traits caractéristiques
[Termes descripteurs IGN] intelligence artificielleRésumé : (auteur) Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science. Numéro de notice : A2020-108 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542697 date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94708
in International journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 637 - 660[article]
Titre : Challenging deep image descriptors for retrieval in heterogeneous iconographic collections Type de document : Article/Communication Auteurs : Dimitri Gominski, Auteur ; Martyna Poreba Congrès : SUMAC 2019, 1st workshop on Structuring and Understanding of Multimedia heritAge Contents (21 octobre 2019; Nice, France) Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2019 Autre Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Projets : Alegoria / Gouet-Brunet, Valérie Importance : pp 31 - 38 Format : 21 x 30 cm Note générale : bibliographie
Preprint publié sur ArXiv https://arxiv.org/abs/1909.08866v1Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes descripteurs IGN] analyse visuelle
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] collection
[Termes descripteurs IGN] descripteur
[Termes descripteurs IGN] données hétérogènes
[Termes descripteurs IGN] exploration de données
[Termes descripteurs IGN] extraction de traits caractéristiques
[Termes descripteurs IGN] iconographie
[Termes descripteurs IGN] image multi sources
[Termes descripteurs IGN] indexation
[Termes descripteurs IGN] jeu de données
[Termes descripteurs IGN] recherche d'image basée sur le contenuRésumé : (auteur) This article proposes to study the behavior of recent and efficient state-of-the-art deep-learning based image descriptors for content-based image retrieval, facing a panel of complex variations appearing in heterogeneous image datasets, in particular in cultural collections that may involve multi-source, multi-date and multi-view contents. For this purpose, we introduce a novel dataset, namely Alegoria dataset, consisting of 12,952 iconographic contents representing landscapes of the French territory, and encapsultating a large range of intra-class variations of appearance which were finely labelled. Six deep features (DELF, NetVLAD, GeM, MAC, RMAC, SPoC) and a hand-crafted local descriptor (ORB) are evaluated against these variations. Their performance are discussed, with the objective of providing the reader with research directions for improving image description techniques dedicated to complex heterogeneous datasets that are now increasingly present in topical applications targeting heritage valorization. Numéro de notice : C2019-022 Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1145/3347317.3357246 date de publication en ligne : 19/09/2019 En ligne : https://doi.org/10.1145/3347317.3357246 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93623
Titre : Google Earth Engine applications Type de document : Monographie Auteurs : Lalit Kumar, Editeur scientifique ; Onisimo Mutanga, Editeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2019 Importance : 420 p. Format : 17 x 25 cm ISBN/ISSN/EAN : 978-3-03897-885-5 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Information géographique
[Termes descripteurs IGN] base de données d'images
[Termes descripteurs IGN] Google Earth Engine
[Termes descripteurs IGN] image 3D
[Termes descripteurs IGN] image aérienne
[Termes descripteurs IGN] image satellite
[Termes descripteurs IGN] information géographique numérique
[Termes descripteurs IGN] informatique en nuage
[Termes descripteurs IGN] moteur de recherche
[Termes descripteurs IGN] surveillance écologique
[Termes descripteurs IGN] système d'information environnementale
[Termes descripteurs IGN] traitement de données localiséesRésumé : (éditeur) In a rapidly changing world, there is an ever-increasing need to monitor the Earth's resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth's surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales. Note de contenu : 1- Google Earth Engine applications since inception: usage, trends, and potential
2- Global estimation of biophysical variables from Google Earth Engine platform
3- An operational before-after-control-impact (BACI) designed platform for vegetation monitoring at planetary scale
4- Mapping vegetation and land use types in Fanjingshan national nature reserve using Google Earth Engine
5- A dynamic Landsat derived Normalized Difference Vegetation Index (NDVI) product for the conterminous United States
6- High spatial resolution visual band imagery outperforms medium resolution spectral imagery for ecosystem assessment in the semi-arid Brazilian Sert˜ao
7- Assessing the spatial and occupation dynamics of the Brazilian pasturelands based on the automated classification of MODIS images from 2000 to 2016
8- Towards global-scale seagrass mapping and monitoring using Sentinel-2 on Google Earth Engine: The case study of the Aegean and Ionian Seas
9- BULC-U: Sharpening resolution and improving accuracy of land-use/land-cover classifications in Google Earth Engine
10- Monitoring the impact of land cover change on surface urban heat island through Google
Earth Engine: Proposal of a global methodology, first applications and problems
11- Regional crop gross primary productivity and yield estimation using fused Landsat-MODIS data
12- The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform
13- A cloud-based multi-temporal ensemble classifier to map smallholder farming systems
14- Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on Google Earth Engine
15- SnowCloudHydro — A new framework for forecasting streamflow in snowy, data-scarce regions
16- Flood prevention and emergency response system powered by Google Earth Engine
17- Leveraging the Google Earth Engine for drought assessment using global soil moisture data
18- Multitemporal cloud masking in the Google Earth Engine
19- Historical and operational monitoring of surface sediments in the lower Mekong basin using Landsat and Google Earth Engine cloud computing
20- Mapping mining areas in the Brazilian Amazon using MSI/Sentinel-2 imagery (2017)
21- Estimating satellite-derived bathymetry (SDB) with the Google Earth Engine and Sentinel-2
22- Mean composite fire severity metrics computed with Google Earth Engine offer improved accuracy and expanded mapping potentialNuméro de notice : 25887 Thématique : GEOMATIQUE/INFORMATIQUE Nature : Monographie DOI : 10.3390/books978-3-03897-885-5 En ligne : https://doi.org/10.3390/books978-3-03897-885-5 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95788 Raffinement de la localisation d’images provenant de sites participatifs pour la mise à jour de SIG urbain / Bernard Semaan (2018)
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