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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 Multiagent object-based classifier for high spatial resolution imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
[article]
Titre : Multiagent object-based classifier for high spatial resolution imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Bei Zhao, Auteur ; Liangpei Zhang, Auteur Année de publication : 2014 Article en page(s) : pp 841 - 857 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] image à ultra haute résolution
[Termes IGN] segmentation d'image
[Termes IGN] système multi-agentsRésumé : (Auteur) Object-based classification, including object-based segmentation and classification, has been applied for the classification of high spatial resolution imagery due to the increase in the spatial resolution and the limited spectral resolution. Because of the independent design of the object-based segmentation and classification in many of the traditional object-based classification methods, additional work is required to select the appropriate segmentation algorithms to match the classification algorithms. The object-based segmentation algorithms, e.g., the fractal net evolution approach (FNEA), have been successfully utilized to provide the homogeneous regions, and are the basis of object-based classification. However, the traditional FNEA algorithm is greatly influenced by the global control strategy of the region-growing procedure. In addition, the existing object classification methods take little account of the object context information, which is important for high spatial-resolution image interpretation. To improve the accuracy of the object-based classification, in this paper, a multiagent object-based classification framework (MAOCF) for high-resolution remote sensing imagery is proposed. The proposed approach avoids the issue of segmentation algorithm selection by unifying the processing of object-based segmentation and classification through the use of a 4-tuple agent model. In the uniform framework, a multiagent object-based segmentation (MAOS) algorithm is proposed to optimally control the procedure of object merging. In addition, a MAOC is proposed to utilize the contextual information from the surrounding objects by taking advantage of the benefits of a multiagent system, e.g., strong interaction, high flexibility, and parallel global control capability. Due to the characteristics of a multiagent system, MAOCF has the potential for a parallel computing ability. Three experiments with different types of images were performed to evaluate the performance- of MAOS and MAOC in comparison to other segmentation and classification algorithms: 1) mean-shift segmentation; 2) FNEA; 3) recursive hierarchical segmentation; and 4) the majority voting object-based classification method. The experimental results demonstrate that MAOS and MAOC give a stable performance with high spatial resolution remote-sensing imagery, and are competitive with the other methods. Numéro de notice : A2014-073 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2244604 En ligne : https://doi.org/10.1109/TGRS.2013.2244604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32978
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 841 - 857[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014021 RAB Revue Centre de documentation En réserve L003 Disponible Putting stock in your survey / Bernard Draeyer in GEO: Geoconnexion international, vol 13 n° 2 (february 2014)
[article]
Titre : Putting stock in your survey Type de document : Article/Communication Auteurs : Bernard Draeyer, Auteur ; Christoph Strecha, Auteur Année de publication : 2014 Article en page(s) : pp 32 - 34 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] photogrammétrie aérienneRésumé : (Auteur) How accurate is UAV surveying for determining stockpile volume? The authors took up the challenge of comparing UAV imagery with lidar scans to see how they matched up. Numéro de notice : A2014-059 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32964
in GEO: Geoconnexion international > vol 13 n° 2 (february 2014) . - pp 32 - 34[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 062-2014021 SL Revue Centre de documentation Revues en salle Disponible Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
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Titre : Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping Type de document : Article/Communication Auteurs : Luca Demarchi, Auteur ; Frank Canters, Auteur ; Claude Cariou, Auteur ; Giorgio Licciardi, Auteur ; Jonathan Cheung-Wai Chan, Auteur Année de publication : 2014 Article en page(s) : pp 166 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Airborne Prism Experiment
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image APEX
[Termes IGN] image hyperspectrale
[Termes IGN] Perceptron multicoucheRésumé : (Auteur) Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas. Numéro de notice : A2014-018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32923
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 166 - 179[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty / Xuellang Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)
[article]
Titre : Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty Type de document : Article/Communication Auteurs : Xuellang Zhang, Auteur ; Pending Xiao, Auteur ; Xuezhi Feng, Auteur Année de publication : 2014 Article en page(s) : pp 71 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image à haute résolution
[Termes IGN] image aérienne
[Termes IGN] image Quickbird
[Termes IGN] image Worldview
[Termes IGN] segmentation d'image
[Termes IGN] segmentation hiérarchique
[Termes IGN] segmentation multi-échelleRésumé : (Auteur) A fast hierarchical segmentation method (FHS) for high-resolution remote sensing (HR) image is proposed in the paper. FHS is completely unsupervised. It is characterized by two aspects. First, the hierarchical segmentation process is accelerated by the improved linear nearest neighbor graph (LNNG) model and the segment tree model. It runs faster than other existing hierarchical segmentation methods, and can produce multi-resolution segmentations in time linear to the image size. Second, an adaptive edge penalty function is introduced to formulate the merging criterion, serving as a semantic factor. A set of QuickBird, WorldView, and aerial images is used to test the proposed method. The experiments show that the multi-resolution segmentations produced by FHS can represent objects at different scales very well. Moreover, the adaptive edge penalty function helps to remove meaningless weak edges within objects, enclosing the relation between segments and real-world objects. Numéro de notice : A2014-093 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.1.71 En ligne : https://doi.org/10.14358/PERS.80.1.71 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32998
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 1 (January 2014) . - pp 71 - 80[article]Hierarchical extraction of landslides from multiresolution remotely sensed optical images / Camille Kurtz in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkHyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkImages virtuelles et horizons du regard / Jean-François Coulais (2014)PermalinkMapping a priori defined plant associations using remotely sensed vegetation characteristics / Hans D. Rölofsen in Remote sensing of environment, vol 140 (January 2014)PermalinkMaximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas / Michael Schmitt in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkUAV photogrammetry to monitor dykes-calibration and comparaison to terrestrial Lidar / Vincent Tournadre (2014)PermalinkPermalinkA photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery / Jonathan Lisein in Forests, vol 4 n° 4 (december 2013)PermalinkRadargrammetric registration of airborne multi-aspect SAR data of urban areas / Michael Schmitt in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)PermalinkWavelet-Based Compressed Sensing for SAR Tomography of Forested Areas / Esteban Aguilera in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)Permalink