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scientifique

Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)  

Public [article]  inEuropean journal of remote sensing > vol 54 n° 1 (2021) . - pp 1 - 12 Titre : Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control Type de document : Article/Communication Auteurs : Adolfo Lozano-Tello, Auteur ; Marcos Fernández-Sellers, Auteur ; Elia Quirós, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification pixellaire [Termes IGN] Estrémadure (Espagne) [Termes IGN] image Sentinel-MSI [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] politique agricole commune [Termes IGN] réseau neuronal artificiel [Termes IGN] surface cultivée [Termes IGN] surveillance agricole Résumé : (auteur) The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results. Numéro de notice : A2021-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1858723 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1858723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97012 [article]

Semiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment / Javier Sanchez in Journal of Cultural Heritage, vol 25 (May - June 2017)  

Public [article]  inJournal of Cultural Heritage > vol 25 (May - June 2017) . - pp 21 - 30 Titre : Semiautomatic detection and classification of materials in historic buildings with low-cost photogrammetric equipment Type de document : Article/Communication Auteurs : Javier Sanchez, Auteur ; Elia Quirós, Auteur Année de publication : 2017 Article en page(s) : pp 21 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bande visible [Termes IGN] détection d'objet [Termes IGN] dommage matériel [Termes IGN] façade [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] monument historique [Termes IGN] restauration de bâtiment Résumé : (auteur) The detection of materials and damage in building facades by means of near-infrared digital images is not a widely explored field in architectural research, especially in rehabilitation and historic building surveys. The aim of this work is to study whether spectral classification image methods, which are frequently used in remote sensing land applications (non-contact geophysical techniques), could be applied in the architectural field to detect various construction materials in historic building facades by means of low-cost photogrammetric equipment. Several classification methodologies were applied to different image band combinations, which led to the conclusion that the highest accuracy is obtained with a multiband image composed of visible and near-infrared bands. We also performed a derived measurement of the real surface of the facing material, demonstrating that low-cost instrumentation could be useful in architectural interventions in cultural heritage to identify construction materials in a non-destructive way. Numéro de notice : A2017-230 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.culher.2016.11.017 En ligne : https://doi.org/10.1016/j.culher.2016.11.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85142 [article]

Detection and labeling of sensitive areas in hydrological cartography using vector statistics / Elia Quirós in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 189 - 196 Titre : Detection and labeling of sensitive areas in hydrological cartography using vector statistics Type de document : Article/Communication Auteurs : Elia Quirós, Auteur ; María-Eugenia Polo, Auteur ; Ángel M. Felicísimo, Auteur Année de publication : 2016 Article en page(s) : pp 189 - 196 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] carte hydrographique [Termes IGN] détection automatique [Termes IGN] données vectorielles [Termes IGN] modèle numérique de terrain [Termes IGN] réseau hydrographique [Termes IGN] statistique descriptive [Termes IGN] système d'information géographique Résumé : (Auteur) The recognition and delineation of hydrological stream lines has, traditionally, been a subjective manual task in cartography. However, digital elevation models (DEMs) are nowadays often employed to extract stream lines automatically, via the use of geographic information systems. Whereas the automatic generation of hydrological networks presents errors, their manual recognition can be almost arbitrary. In this paper, we propose a methodology with which to label potentially sensitive zones in the comparison of hydrological cartographic networks. Two different sources were analyzed: a conventional cartographic stream network, and one automatically extracted from a DEM. The 72 500 vectors of displacement, representing the spatial disagreement (or fit) between the stream networks, were also examined. A number of remarkable distributions of large errors were identified that were a cause for alarm; these errors are here denoted by “warnings” and are classified into six different groups. The displacement vectors were also analyzed in terms of modulus and azimuth, thereby allowing the analysis of the isotropy of the spatial displacements. We propose the use of all of the derived information as metadata for hydrological spatial quality, as well as the extension of the methodology to any other type of cartographic element (roads, cadastral, etc.) for which two different vector format information sources are compared. Numéro de notice : A2016-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2453112 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2453112 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79842 [article]

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065-2016011	SL	Revue	Centre de documentation	Revues en salle	Disponible