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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]Robust multisource remote sensing image registration method based on scene shape similarity / Ming Hao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)
[article]
Titre : Robust multisource remote sensing image registration method based on scene shape similarity Type de document : Article/Communication Auteurs : Ming Hao, Auteur ; Jian Jin, Auteur ; Mengchao Zhou, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 725 - 736 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement de modèles conceptuels de données
[Termes IGN] coefficient de corrélation
[Termes IGN] figuré du terrain
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] niveau de gris (image)
[Termes IGN] points homologues
[Termes IGN] superposition d'images
[Termes IGN] temps de pose
[Termes IGN] transformation linéaireRésumé : (Auteur) Image registration is an indispensable component of remote sensing applications, such as disaster monitoring, change detection, and classification. Grayscale differences and geometric distortions often occur among multisource images due to their different imaging mechanisms, thus making it difficult to acquire feature points and match corresponding points. This article proposes a scene shape similarity feature (SSSF) descriptor based on scene shape features and shape context algorithms. A new similarity measure called SSSFncc is then defined by computing the normalized correlation coefficient of the SSSF descriptors between multisource remote sensing images. Furthermore, the tie points between the reference and the sensed image are extracted via a template matching strategy. A global consistency check method is then used to remove the mismatched tie points. Finally, a piecewise linear transform model is selected to rectify the remote sensing image. The proposed SSSFncc aims to extract the scene shape similarity between multisource images. The accuracy of the proposed SSSFncc is evaluated using five pairs of experimental images from optical, synthetic aperture radar, and map data. Registration results demonstrate that the SSSFncc similarity measure is robust enough for complex nonlinear grayscale differences among multisource remote sensing images. The proposed method achieves more reliable registration outcomes compared with other popular methods. Numéro de notice : A2019-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.725 Date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.725 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93989
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 725 - 736[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019101 SL Revue Centre de documentation Revues en salle Disponible Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments / Leigh Tait in Remote sensing, vol 11 n° 19 (October-1 2019)
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Titre : Unmanned aerial vehicles (UAVs) for monitoring macroalgal biodiversity: comparison of RGB and multispectral imaging sensors for biodiversity assessments Type de document : Article/Communication Auteurs : Leigh Tait, Auteur ; Jochen Bind, Auteur ; Hannah Charan-Dixon, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algue
[Termes IGN] biodiversité
[Termes IGN] changement climatique
[Termes IGN] estran
[Termes IGN] habitat (nature)
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] image RVB
[Termes IGN] Kappa de Cohen
[Termes IGN] Nouvelle-Zélande
[Termes IGN] orthophotoplan numérique
[Termes IGN] réflectance spectrale
[Termes IGN] surveillance du littoralRésumé : (auteur) Developments in the capabilities and affordability of unmanned aerial vehicles (UAVs) have led to an explosion in their use for a range of ecological and agricultural remote sensing applications. However, the ubiquity of visible light cameras aboard readily available UAVs may be limiting the application of these devices for fine-scale, high taxonomic resolution monitoring. Here we compare the use of RGB and multispectral cameras deployed aboard UAVs for assessing intertidal and shallow subtidal marine macroalgae to a high taxonomic resolution. Our results show that the diverse spectral profiles of marine macroalgae naturally lend themselves to remote sensing and habitat classification. Furthermore, we show that biodiversity assessments, particularly in shallow subtidal habitats, are enhanced using six-band discrete wavelength multispectral sensors (81% accuracy, Cohen’s Kappa) compared to three-band broad channel RGB sensors (79% accuracy, Cohen’s Kappa) for 10 habitat classes. Combining broad band RGB signals and narrow band multispectral sensing further improved the accuracy of classification with a combined accuracy of 90% (Cohen’s Kappa). Despite notable improvements in accuracy with multispectral imaging, RGB sensors were highly capable of broad habitat classification and rivaled multispectral sensors for classifying intertidal habitats. High spatial scale monitoring of turbid exposed rocky reefs presents a unique set of challenges, but the limitations of more traditional methods can be overcome by targeting ideal conditions with UAVs. Numéro de notice : A2019-553 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11192332 Date de publication en ligne : 08/10/2019 En ligne : https://doi.org/10.3390/rs11192332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94206
in Remote sensing > vol 11 n° 19 (October-1 2019) . - 18 p.[article]Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])
[article]
Titre : Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece Type de document : Article/Communication Auteurs : Maria Kampouri, Auteur ; Polychronis Kolokoussis, Auteur ; Demetre Argialas, Auteur ; Vassilia Karathanassi, Auteur Année de publication : 2019 Article en page(s) : pp 1273 - 1285 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse forestière
[Termes IGN] conservation des ressources forestières
[Termes IGN] écosystème forestier
[Termes IGN] espèce végétale
[Termes IGN] Grèce
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] image Sentinel-MSI
[Termes IGN] indicateur de biodiversité
[Termes IGN] indice de diversité
[Termes IGN] modèle numérique de surface
[Termes IGN] réalité de terrain
[Termes IGN] segmentation d'imageRésumé : (Auteur) The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation. Numéro de notice : A2019-465 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1489424 Date de publication en ligne : 12/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1489424 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93616
in Geocarto international > vol 34 n° 12 [15/09/2019] . - pp 1273 - 1285[article]Partial linear NMF-based unmixing methods for detection and area estimation of photovoltaic panels in urban hyperspectral remote sensing data / Moussa Sofiane Karoui in Remote sensing, vol 11 n° 18 (September 2019)PermalinkDevelopment and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkEnhanced 3D mapping with an RGB-D sensor via integration of depth measurements and image sequences / Bo Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 9 (September 2019)PermalinkExploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)PermalinkGeometric accuracy improvement of WorldView‐2 imagery using freely available DEM data / Mateo Gašparović in Photogrammetric record, vol 34 n° 167 (September 2019)PermalinkImplementing Moran eigenvector spatial filtering for massively large georeferenced datasets / Daniel A. Griffith in International journal of geographical information science IJGIS, vol 33 n° 9 (September 2019)PermalinkIntegration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain / Yolanda Torres in International journal of applied Earth observation and geoinformation, vol 81 (September 2019)PermalinkLearning and adapting robust features for satellite image segmentation on heterogeneous data sets / Sina Ghassemi in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkPreparing the holoLens for user studies : an augmented reality interface for the spatial adjustment of holographic objects in 3D indoor environments / Julian Keil in KN, Journal of Cartography and Geographic Information, vol 69 n° 3 (September 2019)PermalinkSentinel-2 sharpening using a reduced-rank method / Magnus Orn Ulfarsson in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkUnmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition / Sheng Wang in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkIndividual 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])PermalinkImproving public data for building segmentation from Convolutional Neural Networks (CNNs) for fused airborne lidar and image data using active contours / David Griffiths in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkLocal climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkUne nouvelle méthode de vectorisation du cadastre ancien / Antony Chalais in Géomatique expert, n° 129 (août - septembre 2019)PermalinkPyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkMapping the wavelength position of mineral features in hyperspectral thermal infrared data / Christoph Hecker in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)PermalinkOcclusion probability in operational forest inventory field sampling with ForeStereo / Fernando Montes in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 7 (July 2019)PermalinkStructural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)PermalinkSemantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)Permalink