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Termes IGN > sciences naturelles > physique > traitement d'image > analyse d'image numérique > extraction de traits caractéristiques > extraction de la végétation
extraction de la végétationSynonyme(s)détection de la végétation |
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Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm / Abduwasit Ghulam in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm Type de document : Article/Communication Auteurs : Abduwasit Ghulam, Auteur ; Ingrid Porton, Auteur ; Karen Freeman, Auteur Année de publication : 2014 Article en page(s) : pp 174 - 192 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] arbre de décision
[Termes IGN] espèce exotique envahissante
[Termes IGN] extraction de la végétation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forêt équatoriale
[Termes IGN] hauteur des arbres
[Termes IGN] image optique
[Termes IGN] image radar
[Termes IGN] intégration de données
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Madagascar
[Termes IGN] polarimétrie radar
[Termes IGN] réserve naturelle
[Termes IGN] sous-boisRésumé : (Auteur) In this paper, we propose a decision tree algorithm to characterize spatial extent and spectral features of invasive plant species (i.e., guava, Madagascar cardamom, and Molucca raspberry) in tropical rainforests by integrating datasets from passive and active remote sensing sensors. The decision tree algorithm is based on a number of input variables including matching score and infeasibility images from Mixture Tuned Matched Filtering (MTMF), land-cover maps, tree height information derived from high resolution stereo imagery, polarimetric feature images, Radar Forest Degradation Index (RFDI), polarimetric and InSAR coherence and phase difference images. Spatial distributions of the study organisms are mapped using pixel-based Winner-Takes-All (WTA) algorithm, object oriented feature extraction, spectral unmixing, and compared with the newly developed decision tree approach. Our results show that the InSAR phase difference and PolInSAR HH–VV coherence images of L-band PALSAR data are the most important variables following the MTMF outputs in mapping subcanopy invasive plant species in tropical rainforest. We also show that the three types of invasive plants alone occupy about 17.6% of the Betampona Nature Reserve (BNR) while mixed forest, shrubland and grassland areas are summed to 11.9% of the reserve. This work presents the first systematic attempt to evaluate forest degradation, habitat quality and invasive plant statistics in the BNR, and provides significant insights as to management strategies for the control of invasive plants and conversation in the reserve. Numéro de notice : A2014-090 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.12.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.12.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32995
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 174 - 192[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Model-based analysis–synthesis for realistic tree reconstruction and growth simulation / Corina Iovan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
[article]
Titre : Model-based analysis–synthesis for realistic tree reconstruction and growth simulation Type de document : Article/Communication Auteurs : Corina Iovan , Auteur ; Paul-Henri Cournède, Auteur ; Thomas Guyard, Auteur ; Benoit Bayol, Auteur ; Didier Boldo , Auteur ; Matthieu Cord, Auteur Année de publication : 2014 Article en page(s) : pp 1438 - 1450 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] arbre (flore)
[Termes IGN] arbre urbain
[Termes IGN] croissance des arbres
[Termes IGN] détection d'arbres
[Termes IGN] dynamique de la végétation
[Termes IGN] extraction de la végétation
[Termes IGN] image aérienne
[Termes IGN] modèle de croissance végétale
[Termes IGN] reconstruction d'objetRésumé : (auteur) Due to complexity, vegetation analysis and reconstruction of remote sensing data are challenging problems. Using architectural tree models combined with model inputs estimated from aerial image analysis, this paper presents an analysis-synthesis approach for urban vegetation detection, modeling, and reconstruction. Tree species, height, and crown size information are extracted by aerial image analysis. These variables serve for model inversion to retrieve plant age, climatic growth conditions, and competition with neighbors. Functional-structural individual-based tree models are used to reconstruct and visualize virtual trees and their time evolutions realistically in a 3-D viewer rendering the models with geographical coordinates in the reconstructed scene. Our main contributions are: 1) a novel approach for generating plant models in 3-D reconstructed scenes based on the analysis of the geometric properties of the data, and 2) a modeling workflow for the reconstruction and growth simulation of vegetation in urban or natural environments. Numéro de notice : A2014-815 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2251467 Date de publication en ligne : 12/04/2013 En ligne : https://doi.org/10.1109/TGRS.2013.2251467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92035
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1438 - 1450[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Multiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)
[article]
Titre : Multiple-entity based classification of airborne laser scanning data in urban areas Type de document : Article/Communication Auteurs : S. Xu, Auteur ; M. George Vosselman, Auteur ; Sander J. Oude Elberink, Auteur Année de publication : 2014 Article en page(s) : pp 1 - 15 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse multicritère
[Termes IGN] classificateur paramétrique
[Termes IGN] classification automatique d'objets
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] image ALOS-PALSAR
[Termes IGN] milieu urbain
[Termes IGN] test de performanceRésumé : (Auteur) There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method. Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified. Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers. Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information. Numéro de notice : A2014-080 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.11.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.11.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32985
in ISPRS Journal of photogrammetry and remote sensing > vol 88 (February 2014) . - pp 1 - 15[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Cartographie et suivi de la densité des arbres de l'arganeraie (Sud-Ouest du Maroc) à partir d'images de télédétection à haute résolution spatiale / Mbark Aouragh in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)
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Titre : Cartographie et suivi de la densité des arbres de l'arganeraie (Sud-Ouest du Maroc) à partir d'images de télédétection à haute résolution spatiale Type de document : Article/Communication Auteurs : Mbark Aouragh, Auteur ; Bernard Lacaze, Auteur ; Micheline Hotyat, Auteur ; et al., Auteur Année de publication : 2013 Conférence : AARSE 2012, 9th international conference of the African Association of Remote Sensing and the Environment 29/10/2012 02/11/2012 El Jadida Maroc open access proceedings Article en page(s) : pp 3 - 9 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] Argania spinosa
[Termes IGN] artefact
[Termes IGN] carte de la végétation
[Termes IGN] classification orientée objet
[Termes IGN] correction géométrique
[Termes IGN] densité de la végétation
[Termes IGN] extraction de la végétation
[Termes IGN] forêt méditerranéenne
[Termes IGN] histogramme
[Termes IGN] houppier
[Termes IGN] image à très haute résolution
[Termes IGN] image Geoeye
[Termes IGN] image Ikonos
[Termes IGN] MarocRésumé : (Auteur) L'étude porte sur la cartographie du couvert arboré de la forêt claire d'arganiers du Sud-Ouest du Maroc. Les données utilisées sont une image Ikonos de 2003 et une image GeoEye de 2011, extraite de Google Earth ; cette dernière est corrigée géométriquement pour être superposable à l'image Ikonos (résolution spatiale 1m). L'approche de classification orientée objet permet de cartographier de façon assez satisfaisante les couronnes des arbres sur les deux images. Cependant la comparaison des résultats des deux classifications laisse apparaître des artefacts et ne peut servir à une analyse diachronique fiable. La solution alternative proposée repose sur l'analyse interactive de l'histogramme bi-varié de deux canaux provenant respectivement de l'image de 2003 et de celle de 2011. Pour la zone étudiée, le couvert arboré a un faible recouvrement (8 % en moyenne) et apparaît stable de 2003 à 2011, avec localement une légère diminution de densité des arbres. Numéro de notice : A2013-678 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.52638/rfpt.2013.24 En ligne : https://doi.org/10.52638/rfpt.2013.24 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32814
in Revue Française de Photogrammétrie et de Télédétection > n° 203 (Juillet 2013) . - pp 3 - 9[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 018-2013031 RAB Revue Centre de documentation En réserve L003 Disponible The influence of scanner parameters on the extraction of tree metrics from FARO Photon 120 terrestrial laser scans / Pyare Pueschel in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)
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Titre : The influence of scanner parameters on the extraction of tree metrics from FARO Photon 120 terrestrial laser scans Type de document : Article/Communication Auteurs : Pyare Pueschel, Auteur Année de publication : 2013 Article en page(s) : pp 58 - 68 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre (flore)
[Termes IGN] extraction de la végétation
[Termes IGN] télémètre laser terrestre
[Termes IGN] troncRésumé : (Auteur) In the present study the influence of the scanner parameters, scan resolution (angular step size), scan speed (number of laser pulses per second), and pulse duration, on tree stem detection, stem diameter and volume extraction from phase-shift FARO Photon 120 TLS data was assessed. Additionally the effects of a data post processing (filtering of raw scan data) were investigated. All analyses were carried out based on single and merged scan data. It could be shown that scan speed, pulse duration and data filtering only marginally affect stem detection rates and stem diameter and volume estimation accuracies. By contrast scan resolution was found to have an effect, the magnitude of which, however, is range-dependent. For example mean stem detection rates for the three different scan resolutions tested were found to be equal in near range, but decreased more strongly for the lower scan resolutions in far range. With regard to the stem diameter extraction, scan resolution did not affect stem diameter at breast height (DBH) estimation accuracy, but limited the range within which DBH could be reliably extracted. The root mean squared error (RMSE) for DBH extracted from the single scan data was found to be significantly larger compared to the RMSE for DBH extracted from the merged scan data. Single scan data yielded stem volume estimates with lower accuracies, too. This study demonstrated that it is possible to maximize sampling efficiency by using scanner parameter sets with low scanning times (i.e., low scan resolution, high scan speed) without significantly losing estimation accuracy. If maximum accuracy is desired for both DBH and stem volume, the acquisition of multiple scans with a subsequent data merging is required. Numéro de notice : A2013-179 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32317
in ISPRS Journal of photogrammetry and remote sensing > vol 78 (April 2013) . - pp 58 - 68[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013041 RAB Revue Centre de documentation En réserve L003 Disponible Contribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)PermalinkLa télédétection pour la cartographie de la trame verte en milieu agricole : Évaluation des potentialités d’images multi-angulaires à très haute résolution spatiale / David Sheeren in Revue internationale de géomatique, vol 22 n° 4 (décembre 2012 – février 2013)PermalinkBuilt-up and vegetation extraction and density mapping using WorldView-II / A. Kumar in Geocarto international, vol 27 n° 7 (November 2012)PermalinkBuilding detection in complex scenes thorough effective separation of buildings from trees / M. Awrangjeb in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 7 (July 2012)PermalinkDétection et identification de zones de végétation arborée: utilisation conjointe d'images satellite RapidEye et de données BDOrtho / François Tassin (2012)PermalinkTraitements numériques des images de télédétection, Vol. 3. Traitements appliqués à la photo-interprétation / Olivier de Joinville (2012)PermalinkMulti-level filtering segmentation to measure individual tree parameters based on Lidar data: Application to a mountainous forest with heterogeneous stands / Cédric Vega in International journal of applied Earth observation and geoinformation, vol 13 n° 4 (August 2011)PermalinkDétection et caractérisation de la végétation en milieu urbain à partir d'images aériennes haute résolution / Corina Iovan (2009)PermalinkModélisation de la végétation en milieu urbain : détection et caractérisation à partir d'images aériennes haute résolution couleur et infra-rouge / Corina Iovan in Revue Française de Photogrammétrie et de Télédétection, n° 189 (Mars 2008)PermalinkAutomatic extraction and classification of vegetation areas from high resolution images in urban areas / Corina Iovan (2007)Permalink