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Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)
[article]
Titre : Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle Type de document : Article/Communication Auteurs : Steven E. Franklin, Auteur ; Oumer S. Ahmed, Auteur ; Griffin Williams, Auteur Année de publication : 2017 Article en page(s) : pp 501 - 507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] Canada
[Termes IGN] classification automatique
[Termes IGN] drone
[Termes IGN] espèce végétale
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Ontario (Canada)
[Termes IGN] Pinophyta
[Termes IGN] semis de pointsRésumé : (auteur) Object-based image analysis and machine learning classification procedures, after field calibration and photogrammetric processing of consumer-grade unmanned aerial vehicle (UAV) digital camera data, were implemented to classify tree species in a conifer forest in the Great Lakes/St Lawrence Lowlands Ecoregion, Ontario, Canada. A red-green-blue (RGB) digital camera yielded approximately 72 percent classification accuracy for three commercial tree species and one conifer shrub. Accuracy improved approximately 15 percent, to 87 percent overall, with higher radiometric quality data acquired separately using a digital camera that included near infrared observations (at a lower spatial resolution). Interpretation of the point cloud, spectral, texture and object (tree crown) classification Variable Importance (VI) selected by a machine learning algorithm suggested a good correspondence with the traditional aerial photointerpretation cues used in the development of well-established large-scale photography northern conifer elimination keys, which use three-dimensional crown shape, spectral response (tone), texture derivatives to quantify branching characteristics, and crown size, development and outline features. These results suggest that commonly available consumer-grade UAV-based digital cameras can be used with object-based image analysis to obtain acceptable conifer species classification accuracy to support operational forest inventory applications. Numéro de notice : A2017-434 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.83.7.501 En ligne : https://doi.org/10.14358/PERS.83.7.501 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86338
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 7 (July 2017) . - pp 501 - 507[article]Superresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction / Muhammad Haris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)
[article]
Titre : Superresolution for UAV images via adaptive multiple sparse representation and its application to 3-D reconstruction Type de document : Article/Communication Auteurs : Muhammad Haris, Auteur ; Takuya Watanabe, Auteur ; Liu Fan, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4047 - 4058 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] agriculture
[Termes IGN] carte thématique
[Termes IGN] drone
[Termes IGN] image à haute résolution
[Termes IGN] image multi sources
[Termes IGN] rapport signal sur bruit
[Termes IGN] reconstruction 3D
[Termes IGN] série temporelleRésumé : (Auteur) We propose a superresolution (SR) algorithm based on adaptive sparse representation via multiple dictionaries for images taken by unmanned aerial vehicles (UAVs). The SR attainable through the proposed algorithm can increase the precision of 3-D reconstruction from UAV images, enabling the production of high-resolution images for constructing high-frequency time series and for high-precision digital mapping in agriculture. The basic idea of the proposed method is to use a field server or ground-based camera to take training images and then construct multiple pairs of dictionaries based on selective sparse representations to reduce instability during the sparse coding process. The dictionaries are classified on the basis of the edge orientation into five clusters: 0, 45, 90, 135, and nondirection. The proposed method is expected to reduce blurring, blocking, and ringing artifacts especially in edge areas. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Our experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms based on qualitative and quantitative analysis. In the end, we demonstrate the effectiveness of our proposed method to increase the precision of 3-D reconstruction from UAV images. Numéro de notice : A2017-491 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2687419 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2687419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86420
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 4047 - 4058[article]An accelerated image matching technique for UAV orthoimage registration / Chung-Hsien Tsai in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
[article]
Titre : An accelerated image matching technique for UAV orthoimage registration Type de document : Article/Communication Auteurs : Chung-Hsien Tsai, Auteur ; Yu-Ching Lin, Auteur Année de publication : 2017 Article en page(s) : pp 130 - 145 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse spatiale
[Termes IGN] appariement d'images
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] orthoimageRésumé : (Auteur) Using an Unmanned Aerial Vehicle (UAV) drone with an attached non-metric camera has become a popular low-cost approach for collecting geospatial data. A well-georeferenced orthoimage is a fundamental product for geomatics professionals. To achieve high positioning accuracy of orthoimages, precise sensor position and orientation data, or a number of ground control points (GCPs), are often required. Alternatively, image registration is a solution for improving the accuracy of a UAV orthoimage, as long as a historical reference image is available. This study proposes a registration scheme, including an Accelerated Binary Robust Invariant Scalable Keypoints (ABRISK) algorithm and spatial analysis of corresponding control points for image registration. To determine a match between two input images, feature descriptors from one image are compared with those from another image. A “Sorting Ring” is used to filter out uncorrected feature pairs as early as possible in the stage of matching feature points, to speed up the matching process. The results demonstrate that the proposed ABRISK approach outperforms the vector-based Scale Invariant Feature Transform (SIFT) approach where radiometric variations exist. ABRISK is 19.2 times and 312 times faster than SIFT for image sizes of 1000 × 1000 pixels and 4000 × 4000 pixels, respectively. ABRISK is 4.7 times faster than Binary Robust Invariant Scalable Keypoints (BRISK). Furthermore, the positional accuracy of the UAV orthoimage after applying the proposed image registration scheme is improved by an average of root mean square error (RMSE) of 2.58 m for six test orthoimages whose spatial resolutions vary from 6.7 cm to 10.7 cm. Numéro de notice : A2017-333 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85486
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 130 - 145[article]Réservation
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[article]
Titre : Drones: climbing to the next level Type de document : Article/Communication Auteurs : François Gervaix, Auteur Année de publication : 2017 Article en page(s) : pp 18 - 20 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] droneRésumé : (éditeur) Scarcely a week goes by without drones making the headlines. Geoconnexion caught up with Francois Gervaix, surveying product manager at sensefly, to get a sense of what the company is doing and where the industry is headed. Numéro de notice : A2017-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85010
in GEO: Geoconnexion international > vol 16 n° 6 (June2017) . - pp 18 - 20[article]I’m walking here! Checking the accuracy of an inertial-based pedestrian navigation system with a drone / Marcin Uradzinski in GPS world, vol 28 n° 6 (June 2017)
[article]
Titre : I’m walking here! Checking the accuracy of an inertial-based pedestrian navigation system with a drone Type de document : Article/Communication Auteurs : Marcin Uradzinski, Auteur ; Hang Guo, Auteur ; Clifford Mugnier, Auteur Année de publication : 2017 Article en page(s) : pp 58 - 64 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] drone
[Termes IGN] filtre de Kalman
[Termes IGN] navigation à l'estime
[Termes IGN] navigation pédestre
[Termes IGN] piéton
[Termes IGN] positionnement en intérieurRésumé : (Auteur) Satellite navigation systems have achieved great success in personal positioning applications. Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem. One such solution is a personal pedestrian dead reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians. Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment - problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength. In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors. Numéro de notice : A2017-294 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85332
in GPS world > vol 28 n° 6 (June 2017) . - pp 58 - 64[article]Low aerial imagery – an assessment of georeferencing errors and the potential for use in environmental inventory / Maciej Smaczyński in Geodesy and cartography, vol 66 n° 1 (June 2017)PermalinkThe power of UAVs / Jakub Karas in GEO: Geoconnexion international, vol 16 n° 6 (June2017)PermalinkConférence drones CNAM / Anonyme in Géomatique expert, n° 116 (mai - juin 2017)PermalinkJournées de la Recherche IGN à l'ENSG / Anonyme in Géomatique expert, n° 116 (mai - juin 2017)PermalinkLightweight UAV with on-board photogrammetry and single-frequency GPS positioning for metrology applications / Mehdi Daakir in ISPRS Journal of photogrammetry and remote sensing, vol 127 (May 2017)PermalinkForestry applications of UAVs in Europe: a review / Chiara Torresan in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)PermalinkUAS, sensors, and data processing in agroforestry: a review towards practical applications / Luis Padua in International Journal of Remote Sensing IJRS, vol 38 n° 8-10 (April 2017)PermalinkActive interseismic shallow deformation of the Pingting terraces (Longitudinal Valley – Eastern Taiwan) from UAV high-resolution topographic data combined with InSAR time series / Benoit Deffontaines in Geomatics, Natural Hazards and Risk, vol 8 (2017)PermalinkAssessing the impacts of canopy openness and flight parameters on detecting a sub-canopy tropical invasive plant using a small unmanned aerial system / Ryan L. Perroy in ISPRS Journal of photogrammetry and remote sensing, vol 125 (March 2017)PermalinkIndustrialisation des processus d'extraction d'objets à partir de données photogrammétriques par drones / Jérémie Brossard in XYZ, n° 150 (mars - mai 2017)Permalink