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Robust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
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[article]
Titre : Robust object-based multipass InSAR deformation reconstruction Type de document : Article/Communication Auteurs : Jian Kang, Auteur ; Yuanyuan Wang, Auteur ; Marco Körner, Auteur ; Xiao Xiang Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 4239 - 4251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification orientée objet
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] inversion
[Termes IGN] pont
[Termes IGN] surveillance géologique
[Termes IGN] tenseurRésumé : (Auteur) Deformation monitoring by multipass synthetic aperture radar (SAR) interferometry (InSAR) is, so far, the only imaging-based method to assess millimeter-level deformation over large areas from space. Past research mostly focused on the optimal retrieval of deformation parameters on the basis of a single pixel or a pixel cluster. Only until recently, the first demonstration of object-based urban infrastructure monitoring by fusing InSAR and the semantic classification labels derived from optical images was presented by Wang et al. Given such classification labels in the SAR image, we propose a general framework for object-based InSAR parameter retrieval, where the parameters of the whole object are jointly estimated by the inversion of a regularized tensor model instead of pixelwise. Our approach does not assume the stationarity of each sample in the object, which is usually assumed in other pixel cluster-based methods, such as SqueeSAR. In addition, to handle outliers in real data, a robust phase recovery step prior to parameter retrieval is also introduced. In typical settings, the proposed method outperforms the current pixelwise estimators, e.g., periodogram, by a factor of several tens in the accuracy of the linear deformation estimates. Last but not least, for a practical demonstration on bridge monitoring, we present a full workflow of long-term bridge monitoring using the proposed approach. Numéro de notice : A2017-492 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2684424 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2684424 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86422
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4239 - 4251[article]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)
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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]Change detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)
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Titre : Change detection in forests and savannas using statistical analysis based on geographical objects Type de document : Article/Communication Auteurs : Lucilia Rezende Leite, Auteur ; Luis Marcelo Tavares de Carvalho, Auteur ; Fortunato Menezes da Silva, Auteur Année de publication : 2017 Article en page(s) : pp 284 - 295 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] Brésil
[Termes IGN] classification par la distance de Mahalanobis
[Termes IGN] détection de changement
[Termes IGN] forêt équatoriale
[Termes IGN] image Landsat-TM
[Termes IGN] khi carré
[Termes IGN] réflectance végétale
[Termes IGN] savane
[Termes IGN] segmentation d'imageRésumé : (auteur) The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study. Numéro de notice : A2017-394 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1590/S1982-21702017000200018 En ligne : http://dx.doi.org/10.1590/S1982-21702017000200018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85910
in Boletim de Ciências Geodésicas > vol 23 n° 2 (abr - jun 2017) . - pp 284 - 295[article]Estimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data / Joachim Maack in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)
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Titre : Estimating the spatial distribution, extent and potential lignocellulosic biomass supply of Trees Outside Forests in Baden-Wuerttemberg using airborne LiDAR and OpenStreetMap data Type de document : Article/Communication Auteurs : Joachim Maack, Auteur ; Marcus Lingenfelder, Auteur ; Christina Eilers, Auteur ; Thomas Smaltschinski, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 118 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] arbre hors forêt
[Termes IGN] Bade-Wurtemberg (Allemagne)
[Termes IGN] biomasse
[Termes IGN] classification
[Termes IGN] détection d'objet
[Termes IGN] distribution spatiale
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données localisées des bénévoles
[Termes IGN] inventaire de la végétation
[Termes IGN] lasergrammétrie
[Termes IGN] OpenStreetMap
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Trees Outside Forests (TOF) represent a source of lignocellulosic biomass that has received increasing attention in the recent years. While some studies have already investigated the potential of TOF in Germany, a spatial explicit analysis, specifically for Baden-Wuerttemberg, is still lacking. We used a unique wall-to-wall airborne Light Detection and Ranging (LiDAR) dataset combined with OpenStreetMap (OSM) data to map and classify TOF of the federal state of Baden-Wuerttemberg (∼35.000 km2) in south-western Germany. Furthermore, from annual biomass potentials of TOF areas collected from available literature, we calculated the mean annual biomass supply for all TOF areas in Baden-Wuerttemberg. This combination of remote sensing-based classification and available literature resulted in a mean annual biomass supply between ∼490,000–730,000 t from TOF in Baden-Wuerttemberg. The classification congruence on three reference sites was very high (∼99%) using a simple filter technique applied to the LiDAR data and masking man-made objects using OSM data. In contrast, the available literature revealed a high variability of biomass potentials, supporting the demand for an inventory system. Still, the results demonstrate the applicability of LiDAR based vegetation mapping and the value of OSM data in Baden-Wuerttemberg to detect man-made objects. Numéro de notice : A2017-367 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.02.002 En ligne : https://doi.org/10.1016/j.jag.2017.02.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85795
in International journal of applied Earth observation and geoinformation > vol 58 (June 2017) . - pp 118 - 125[article]Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
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Titre : Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms Type de document : Article/Communication Auteurs : Lien T.H. Pham, Auteur Année de publication : 2017 Article en page(s) : pp 86 - 97 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse forestière
[Termes IGN] carte thématique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de changement
[Termes IGN] image SPOT 4
[Termes IGN] image SPOT 5
[Termes IGN] mangrove
[Termes IGN] surveillance de la végétation
[Termes IGN] teneur en carbone
[Termes IGN] texture d'image
[Termes IGN] Viet NamRésumé : (Auteur) Mangrove forests are well-known for their provision of ecosystem services and capacity to reduce carbon dioxide concentrations in the atmosphere. Mapping and quantifying mangrove biomass is useful for the effective management of these forests and maximizing their ecosystem service performance. The objectives of this research were to model, map, and analyse the biomass change between 2000 and 2011 of mangrove forests in the Cangio region in Vietnam. SPOT 4 and 5 images were used in conjunction with object-based image analysis and machine learning algorithms. The study area included natural and planted mangroves of diverse species. After image preparation, three different mangrove associations were identified using two levels of image segmentation followed by a Support Vector Machine classifier and a range of spectral, texture and GIS information for classification. The overall classification accuracy for the 2000 and 2011 images were 77.1% and 82.9%, respectively. Random Forest regression algorithms were then used for modelling and mapping biomass. The model that integrated spectral, vegetation association type, texture, and vegetation indices obtained the highest accuracy (R2adj = 0.73). Among the different variables, vegetation association type was the most important variable identified by the Random Forest model. Based on the biomass maps generated from the Random Forest, total biomass in the Cangio mangrove forest increased by 820,136 tons over this period, although this change varied between the three different mangrove associations. Numéro de notice : A2017-332 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.03.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.03.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85485
in ISPRS Journal of photogrammetry and remote sensing > vol 128 (June 2017) . - pp 86 - 97[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017063 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017062 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Object-based analysis of multispectral airborne laser scanner data for land cover classification and map updating / Leena Matikainen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
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)
PermalinkApplying detection proposals to visual tracking for scale and aspect ratio adaptability / Dafei Huang in International journal of computer vision, vol 122 n° 3 (May 2017)
PermalinkMise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques / Monique Moine in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)
PermalinkA classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas / Martin Weinmann in Remote sensing, vol 9 n° 3 (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)
PermalinkUnsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
PermalinkAgricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests / M.F.A. Vogels in International journal of applied Earth observation and geoinformation, vol 54 (February 2017)
PermalinkObject-based water body extraction model using Sentinel-2 satellite imagery / Gordana Kaplan in European journal of remote sensing, vol 50 n° 1 (2017)
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