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Automatic canola mapping using time series of Sentinel 2 images / Davoud Ashourloo in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)
[article]
Titre : Automatic canola mapping using time series of Sentinel 2 images Type de document : Article/Communication Auteurs : Davoud Ashourloo, Auteur ; Hamid Salehi Shahrabi, Auteur ; Mohsen Azadbakht, Auteur ; Hossein Aghighi, Auteur ; Hamed Nematollahi, Auteur ; Abbas Alimohammadi, Auteur ; Ali Akbar Matkan, Auteur Année de publication : 2019 Article en page(s) : pp 63 - 76 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agriculture de précision
[Termes IGN] Brassica napus subsp. napus
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] image Sentinel-MSI
[Termes IGN] Iran
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] Oklahoma (Etats-Unis)
[Termes IGN] rendement agricole
[Termes IGN] série temporelleRésumé : (Auteur) Different techniques utilized for mapping various crops are mainly based on using training dataset. But, due to difficulties of access to a well-represented training data, development of automatic methods for detection of crops is an important need which has not been considered as it deserves. Therefore, main objective of present study was to propose a new automatic method for canola (Brassica napus L.) mapping based on Sentinel 2 satellite time series data. Time series data of three study sites in Iran (Moghan, Gorgan, Qazvin) and one site in USA: (Oklahoma), were used. Then, spectral reflectance values of canola in various spectral bands were compared with those of the other crops during the growing season. NDVI, Red and Green spectral bands were successfully applied for automatic identification of canola flowering date using the threshold values. Examination of the fisher function indicated that multiplication of the near-infrared (NIR) band by the sum of red and green bands during the flowering date is an efficient index to differentiate canola from the other crops. The Kappa and overall accuracy (OA) for the four study sites were more than 0.75 and 88%, respectively. Results of this research demonstrated the potential of the proposed approach for canola mapping using time series of remotely sensed data. Numéro de notice : A2019-317 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.08.007 Date de publication en ligne : 09/08/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.08.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93355
in ISPRS Journal of photogrammetry and remote sensing > vol 156 (October 2019) . - pp 63 - 76[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019103 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019102 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Un été brûlant sous l’oeil des satellites / Laurent Polidori in Géomètre, n° 2173 (octobre 2019)
[article]
Titre : Un été brûlant sous l’oeil des satellites Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2019 Article en page(s) : pp 48 - 50 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Amazonie
[Termes IGN] déboisement
[Termes IGN] détection d'anomalie
[Termes IGN] détection de changement
[Termes IGN] image optique
[Termes IGN] image proche infrarouge
[Termes IGN] image radar
[Termes IGN] image satellite
[Termes IGN] image Sentinel
[Termes IGN] incendie de forêtRésumé : (auteur) La variété des capteurs (optique, thermique ou radar) embarqués sur des satellites interdit désormais la dissimulation des évolutions naturelles ou artificielles qui engendrent des transformations des territoires. Le Brésil l'a constaté cet été... Numéro de notice : A2019-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93686
in Géomètre > n° 2173 (octobre 2019) . - pp 48 - 50[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2019091 RAB Revue Centre de documentation En réserve L003 Disponible A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)
[article]
Titre : A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing Type de document : Article/Communication Auteurs : Ran Pelta, Auteur ; Nimrod Carmon, Auteur ; Eyal Ben-Dor, Auteur Année de publication : 2019 Article en page(s) : 15 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] étalonnage de modèle
[Termes IGN] hydrocarbure
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image infrarouge
[Termes IGN] image proche infrarouge
[Termes IGN] Israël
[Termes IGN] Kappa de Cohen
[Termes IGN] pétrole
[Termes IGN] photo-interprétation
[Termes IGN] pollution des sols
[Termes IGN] réflectance du sol
[Termes IGN] spectroscopieRésumé : (auteur) One of the most ubiquitous and detrimental types of environmental contamination in the world is crude oil pollution. When released into either the aquatic or terrestrial environments, this pollution can negatively impact flora and fauna, as well as human health. Hence, a rapid and affordable spatial assessment of the pollution is favored to limit the spill’s effects. Using airborne hyperspectral remote sensing (HRS) for crude oil detection in terrestrial areas has been investigated in previous studies, which mainly relied on heavily oiled artificial samples. These studies and others based their methodologies on the premise that the spectral features of petroleum hydrocarbon (PHC) are clearly observable, which might not be true in all cases. In this study, we aimed at assessing the true potential of using HRS for terrestrial oil spill mapping in a real disaster site in southern Israel, where laboratory and controlled conditions do not apply. Using the AISA SPECIM Fenix1 K sensor, we collected airborne image of the study site and analyzed the data with advanced data mining techniques. Various challenges and limitations arose from the airborne HRS image being taken two and a half years after the crude oil had been released into the environment and exposed to the surface. Here, no spectral features of PHC were detectable in the spectrum, preventing the use of PHC indices and spectral methods developed by others. Nevertheless, by using standardization techniques, vicarious band selection, dimension reduction, multivariate calibration, and supervised machine-learning, we were able to successfully distinguish between contaminated pixels from non-contaminated ones. Classification accuracy metrics of overall accuracy, sensitivity, specificity, and Kappa yielded good results of 0.95, 0.95, 0.95 and 0.9, respectively, for cross-validation, and 0.93, 0.91, 0.94 and 0.85, for the validation dataset. Classified image and test scenes also showed strong agreement with an orthophoto image taken several days after the disaster, when the pollution was clearly visible. Thus, we conclude that HRS technology can detect PHC traces in an oil spill site, even under the most challenging conditions. Numéro de notice : A2019-475 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.101901 Date de publication en ligne : 22/06/2019 En ligne : https://doi.org/10.1016/j.jag.2019.101901 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93636
in International journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - 15 p.[article]Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])
[article]
Titre : Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data Type de document : Article/Communication Auteurs : Sitinor Atikah Nordin, Auteur ; Zulkiflee Abd Latif, Auteur ; Hamdan Omar, Auteur Année de publication : 2019 Article en page(s) : pp 1218 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse multibande
[Termes IGN] Asie du sud-est
[Termes IGN] bande rouge
[Termes IGN] canopée
[Termes IGN] capteur hyperspectral
[Termes IGN] carte forestière
[Termes IGN] forêt tropicale
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] image satellite
[Termes IGN] niveau de gris (image)
[Termes IGN] réflectance végétale
[Termes IGN] segmentation d'image
[Termes IGN] teneur en chlorophylle des feuilles
[Termes IGN] tourbièreRésumé : (Auteur) Individual tree crown segmentation is important step for deriving various information for fine-scale analysis of ecological process. However, only several studies have applied tree crown segmentation in tropical forest ecosystems, especially in mixed peat swamp forests. In this study, hyperspectral data were used to detect changes in the biochemical and biophysical characteristics, which are important factors for tree crown segmentation. Principal Component Analysis method was performed to investigate its influence on crown segmentation. Visually Selected PCs, 160 PCs and 160 Spectral Bands image were used and two segmentation techniques; Watershed Transformation and Region Growing segmentation were applied on those images. The highest accuracy was achieved for the crown segmentation is using Region Growing segmentation, based on 1:1 measurement, D value and RMSE value. The results obtained from 160 PCs image using region growing algorithm shows better accuracy with D value of 0.2 (80% accuracy, 20% error) and RMSE of 9.9 m2. Numéro de notice : A2019-463 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1475511 Date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1475511 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93605
in Geocarto international > vol 34 n° 11 [15/08/2019] . - pp 1218 - 1236[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2019111 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Satellite information classification and interpretation Type de document : Monographie Auteurs : Rustam B. Rustamov, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2019 Importance : 172 p. ISBN/ISSN/EAN : 978-1-83880-793-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] image proche infrarouge
[Termes IGN] réseau neuronal artificiel
[Termes IGN] télédétection spatialeRésumé : (Editeur) Without a doubt, understanding what we must do to save our home, our planet, and how we are to do it is of the gravest importance for the present generation and the next. Clearly, advances won through space technology and applications of the same to the study of Earth play an excellent and vital role in classification and interpretation of the processes taking place on the Earth and in space. Today, space technology helps us understand Earth and how we can support and manage its state, to keep it in working condition under the current circumstances.How can we do this? Obviously, we must use appropriate methods and instruments to collect the information we need. In the meantime, it is necessary to develop systems to analyze and process the data collected. Note de contenu : 1. Introductory Chapter: Aerospace Information Classification
2. Pan-sharpening Using Spatial-frequency Method
3. Lossy Compression of Remote Sensing Images with Controllable Distortions
4. Reverse Satellite Transionospheric Sounding: Advantages and Prospects
5. High-Resolution Satellite Imagery Classification for Urban Form Detection
6. Water Management in Irrigation Systems by Using Satellite Information
7. Validation of Satellite (TMPA and IMERG) Rainfall Products with the IMD Gridded Data Sets over Monsoon Core Region of India
8. Near- and Middle-Infrared Monitoring of Burned Areas from Space
9. The Use of Visible Geostationary Operational Meteorological Satellite Imagery in Mapping the Water Balance over Puerto Rico for Water Resource ManagementNuméro de notice : 26311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.77202 Date de publication en ligne : 03/10/2019 En ligne : https://doi.org/10.5772/intechopen.77202 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95088 Télédétection multispectrale et hyperspectrale des eaux littorales turbides / Morgane Larnicol (2018)PermalinkSemiautomatic 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)PermalinkUnsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkThermal infrared reveals vegetation stress / Thomas A. Groen in GIM international [en ligne], vol 30 n° 6 (June 2016)PermalinkReal-time atmospheric correction of AVIRIS-NG imagery / Brian D. Bue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkIs the variability of key wood properties linked with the variability of key architectural traits? Case of planted Teak in Togo regarding thinning and provenance / Kodjo Tondjo in Annals of Forest Science, vol 72 n° 6 (September 2015)PermalinkA novel method to correct for wood MOE ultrasonics and NIRS measurements on increment cores in Liquidambar styraciflua L / Herizo Rakotovololonalimanana in Annals of Forest Science, vol 72 n° 6 (September 2015)PermalinkApport du LiDAR dans le géoréférencement d'images hyperspectrales en vue d'un couplage LiDAR/hyperspectral / Antoine Ba in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)PermalinkContribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)PermalinkAnalysis of the spectral variability of urban materials for classification : A case study over Toulouse (France) / Sophie Lacherade (2005)Permalink