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Surface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data / Nadia Ouaadi (2020)
Titre : Surface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data Type de document : Article/Communication Auteurs : Nadia Ouaadi, Auteur ; Lionel Jarlan, Auteur ; Jamal Ezzahar, Auteur ; Mehrez Zribi, Auteur ; Saïd Khabba, Auteur ; Elhoussaine Bouras, Auteur ; Pierre-Louis Frison , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : M2GARSS 2020, Mediterranean and Middle-East Geoscience and Remote Sensing Symposium 09/03/2020 11/03/2020 Tunis Tunisie Proceedings IEEE Importance : pp 212 - 215 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] cultures irriguées
[Termes IGN] humidité du sol
[Termes IGN] image Sentinel-SAR
[Termes IGN] Maroc
[Termes IGN] polarimétrie radar
[Termes IGN] surveillance agricole
[Termes IGN] zone semi-arideRésumé : (auteur) The sensitivity of the backscattering coefficient and the interferometric coherence to surface soil moisture changes and wheat crops growth was analyzed using the time series derived from C-band Sentinel-1. Results show that the interferometric coherence is sensitive to wheat biomass while the backscatter intensity is more influenced by the surface soil moisture (SSM) changes. A new method to retrieve SSM combining the interferometric coherence and C-band backscattering coefficient acquired by Sentinel-1 is tested over two irrigated wheat plots during two growth seasons around Marrakech city (center of Morocco). It is shown that SSM can be estimated with a reasonable accuracy from sowing to harvest (R=0.65; RMSE =0.07m3/m3; bias =0.01m3/m3). These results enhance the potentialities of Sentinel1 data for SSM retrieval even in the presence of a dense canopy. Numéro de notice : C2020-034 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/M2GARSS47143.2020.9105282 Date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.1109/M2GARSS47143.2020.9105282 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99679 Temporal decorrelation at C- and L-band over olive tree plantations: first insights from the Marocscat campaigns / Ludovic Villard (2020)
Titre : Temporal decorrelation at C- and L-band over olive tree plantations: first insights from the Marocscat campaigns Type de document : Article/Communication Auteurs : Ludovic Villard, Auteur ; Adnane Chakir , Auteur ; Pascal Fanise, Auteur ; Nadia Ouaadi, Auteur ; Jamal Ezzahar, Auteur ; Saïd Khabba, Auteur ; Mohamed Kasbani, Auteur ; Valérie Le Dantec, Auteur ; Mehrez Zribi, Auteur ; Salah Er-Raki, Auteur ; Pierre-Louis Frison , Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : M2GARSS 2020, Mediterranean and Middle-East Geoscience and Remote Sensing Symposium 09/03/2020 11/03/2020 Tunis Tunisie Proceedings IEEE Importance : pp 57 - 60 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] décorrélation
[Termes IGN] diffusomètre
[Termes IGN] Maroc
[Termes IGN] Olea europaea
[Termes IGN] surveillance agricoleRésumé : (auteur) This paper addresses the question of temporal decorrelation at C and L-band in the case of olive tree plantations, considering the very recent tower-based experiments in Morocco, as well as the preliminary results conducted in preparation phases. Based on the expertise gained from the TropiScat-1&2 campaigns, the presented experiments also consist in the acquisition of dense time series using a Vector Network Analyser (VNA) connected to several antennas, herein dedicated to L and C-band data. Likewise, this experiment benefit from auxiliary measurements (meteorological and in-situ data) in order to better understand the processes behind temporal decorrelation, which remains a key issue for future repeat-pass, tandem or geostationary missions. Our first results highlight the importance of the acquisition time during the day, and also rise the challenge on how to disentangle the combined effects of water content variations and wind induced displacements driven by solar convection. Numéro de notice : C2020-035 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/M2GARSS47143.2020.9105196 Date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.1109/M2GARSS47143.2020.9105196 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99680 Water stress detection over irrigated wheat crops in semi-arid areas using the diurnal differences of Sentinel-1 backscatter / Nadia Ouaadi (2020)
Titre : Water stress detection over irrigated wheat crops in semi-arid areas using the diurnal differences of Sentinel-1 backscatter Type de document : Article/Communication Auteurs : Nadia Ouaadi, Auteur ; Lionel Jarlan, Auteur ; Jamal Ezzahar, Auteur ; Saïd Khabba, Auteur ; Valérie Le Dantec, Auteur ; Zoubair Rafi, Auteur ; Mehrez Zribi, Auteur ; Pierre-Louis Frison , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2020 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : M2GARSS 2020, Mediterranean and Middle-East Geoscience and Remote Sensing Symposium 09/03/2020 11/03/2020 Tunis Tunisie Proceedings IEEE Importance : pp 306 - 309 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] cultures irriguées
[Termes IGN] image Sentinel-SAR
[Termes IGN] Maroc
[Termes IGN] stress hydrique
[Termes IGN] surveillance agricole
[Termes IGN] teneur en eau de la végétation
[Termes IGN] variation diurne
[Termes IGN] zone semi-arideRésumé : (auteur) This work aims to investigate the sensitivity of the diurnal differences of radar backscatter to diurnal changes in the vegetation water content (VWC). Sentinel-1 backscattering coefficient differences between two orbits (morning and evening) are analyzed over an irrigated and voluntarily stressed wheat field. A physical model of backscatter prediction is evaluated for wheat and used to examine the sensitivity of radar differences to the VWC for a range of surface soil moisture and biomass conditions. Results highlight the potential of C-band diurnal differences for the monitoring the water stress over agricultural canopies. Numéro de notice : C2020-036 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/M2GARSS47143.2020.9105171 Date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.1109/M2GARSS47143.2020.9105171 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99685 Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
[article]
Titre : Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture Type de document : Article/Communication Auteurs : Dino Lenco, Auteur ; Roberto Interdonato, Auteur ; Raffaele Gaetano, Auteur ; Ho Tong Minh Dinh, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] Burkina Faso
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] occupation du sol
[Termes IGN] Réunion, île de la
[Termes IGN] série temporelle
[Termes IGN] utilisation du solRésumé : (auteur) The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal. Numéro de notice : A2019-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.016 Date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94186
in ISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019)[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019123 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019122 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
[article]
Titre : Half a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning Type de document : Article/Communication Auteurs : Benjamin Kellenberger, Auteur ; Diego Marcos, Auteur ; Sylvain Lobry, Auteur ; Devis Tuia, Auteur Année de publication : 2019 Article en page(s) : pp 9524 - 9533 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection d'objet
[Termes IGN] données localisées
[Termes IGN] échantillonnage de données
[Termes IGN] faune locale
[Termes IGN] image captée par drone
[Termes IGN] Namibie
[Termes IGN] objet mobile
[Termes IGN] réalité de terrain
[Termes IGN] recensementRésumé : (auteur) We present an Active Learning (AL) strategy for reusing a deep Convolutional Neural Network (CNN)-based object detector on a new data set. This is of particular interest for wildlife conservation: given a set of images acquired with an Unmanned Aerial Vehicle (UAV) and manually labeled ground truth, our goal is to train an animal detector that can be reused for repeated acquisitions, e.g., in follow-up years. Domain shifts between data sets typically prevent such a direct model application. We thus propose to bridge this gap using AL and introduce a new criterion called Transfer Sampling (TS). TS uses Optimal Transport (OT) to find corresponding regions between the source and the target data sets in the space of CNN activations. The CNN scores in the source data set are used to rank the samples according to their likelihood of being animals, and this ranking is transferred to the target data set. Unlike conventional AL criteria that exploit model uncertainty, TS focuses on very confident samples, thus allowing quick retrieval of true positives in the target data set, where positives are typically extremely rare and difficult to find by visual inspection. We extend TS with a new window cropping strategy that further accelerates sample retrieval. Our experiments show that with both strategies combined, less than half a percent of oracle-provided labels are enough to find almost 80% of the animals in challenging sets of UAV images, beating all baselines by a margin. Numéro de notice : A2019-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2927393 Date de publication en ligne : 20/08/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2927393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94592
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 12 (December 2019) . - pp 9524 - 9533[article]Utilisation des SIG et de la télédétection pour la cartographie de la susceptibilité aux mouvements d'instabilité de versant dans l'Ouest montagneux de la Côte d'Ivoire / Boyossoro Hélène Kouadio in Revue Française de Photogrammétrie et de Télédétection, n° 221 (novembre 2019)PermalinkEvolution of sand encroachment using supervised classification of Landsat data during the period 1987–2011 in a part of Laâyoune-Tarfaya basin of Morocco / Ali Aydda in Geocarto international, vol 34 n° 13 ([15/10/2019])PermalinkTransformation 3D des coordonnées GPS en coordonnées Nord Sahara avec la MRE / Medjahed Sid Ahmed in Géomatique expert, n° 130-131 (octobre - décembre 2019)PermalinkTopographie et archéologie, du cordeau au tout numérique : plus de 40 ans d'interactions / Bertrand Chazaly in XYZ, n° 160 (septembre 2019)PermalinkModelling geographic accessibility to primary health care facilities : combining open data and geospatial analysis / Olanrewaju Lawal in Geo-spatial Information Science, vol 22 n° 3 (August 2019)PermalinkEvaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data / Charles Otunga in Geocarto international, vol 34 n° 10 ([15/07/2019])PermalinkAccuracy assessment of relative and precise point positioning online GPS processing services / Ahmed El Shouny in Journal of applied geodesy, vol 13 n° 3 (July 2019)PermalinkSensitivity of GPS tropospheric estimates to mesoscale convective systems in West Africa / Samuel Nahmani in Atmospheric chemistry and physics, vol 19 n° 14 (July 2019)PermalinkSpatial information recovery in the desert using LMS-based geodetic network adjustment / Eva Stopková in Survey review, vol 51 n° 367 (July 2019)PermalinkUsing direct transformation approach as an alternative technique to fuse global digital elevation models with GPS/levelling measurements in Egypt / Hossam Talaat Elshambaky in Journal of applied geodesy, vol 13 n° 3 (July 2019)Permalink