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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]

Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)  

Public [article]  inEuropean journal of remote sensing > vol 54 n° 1 (2021) . - pp 13 - 28 Titre : Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam Type de document : Article/Communication Auteurs : Vu Anh Tuan, Auteur ; Nguyen Hong Quang, Auteur ; le Thi Thu Hang, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] bande L [Termes IGN] cartographie des risques [Termes IGN] crue [Termes IGN] image ALOS [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] inondation [Termes IGN] Mekong (fleuve) [Termes IGN] optimisation spatiale [Termes IGN] surveillance hydrologique [Termes IGN] Viet Nam Résumé : (auteur) One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded. Numéro de notice : A2021-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1859340 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1859340 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97015 [article]

Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle / Lucas Santos Santana in European journal of remote sensing, vol 54 n° 1 (2021)  

Public [article]  inEuropean journal of remote sensing > vol 54 n° 1 (2021) . - pp 59 - 71 Titre : Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle Type de document : Article/Communication Auteurs : Lucas Santos Santana, Auteur ; Gabriel Araújo E Silva Ferraz, Auteur ; Diego Bedin Marin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 59 - 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] altitude [Termes IGN] capteur aérien [Termes IGN] géoréférencement [Termes IGN] hauteur de vol [Termes IGN] image captée par drone [Termes IGN] Minas Gerais (Brésil) [Termes IGN] modèle numérique de terrain [Termes IGN] photogrammétrie aérienne [Termes IGN] point d'appui [Termes IGN] précision géométrique (imagerie) Résumé : (auteur) This study aimed to explore the influence of flight altitude, density, and distribution of ground control points (GCPs) on the digital terrain model (DTM) in surveys conducted by unmanned aerial vehicles (UAVs). A total of 144 photogrammetric projects consisting of 399 aerial photos were carried out in a 2 ha area. These photogrammetric projects involved six GCP distributions (edge, center, diagonal, parallel, stratified, and random), six GCP densities, and four flight altitudes (30, 60, 90, and 120 m). The response surface methodology was used to find interference factors and total root-mean-square error (RMSEt) as well. The 60 m flight altitude presented was the most efficient. Central GCP distribution was observed to have low precision. Using stratified and random edge distributions, 10 GCPs are recommended to achieve geometric precision below 0.07 m at any flight height. However, for studies requiring up to 0.07 m precision, the best distribution was parallel with 4 GCPs at any altitude. Diagonal positioning of the GCPs showed RMSEt values below 0.11 m with 4 GCPs at any altitude. A good distribution of GCPs was found to be important, but the density of GCPs per image was more relevant when obtaining a lower RMSEt. Numéro de notice : A2021-155 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1845104 Date de publication en ligne : 10/01/2021 En ligne : https://doi.org/10.1080/22797254.2020.1845104 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97018 [article]