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Simultaneous chain-forming and generalization of road networks / Susanne Wenzel in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 19 - 28 Titre : Simultaneous chain-forming and generalization of road networks Type de document : Article/Communication Auteurs : Susanne Wenzel, Auteur ; Dimitri Bulatov, Auteur Année de publication : 2019 Article en page(s) : pp 19 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithme de Douglas-Peucker [Termes IGN] analyse de groupement [Termes IGN] Autriche [Termes IGN] axe médian [Termes IGN] classification bayesienne [Termes IGN] extraction du réseau routier [Termes IGN] itération [Termes IGN] mise à jour automatique [Termes IGN] Munich [Termes IGN] objet géographique linéaire [Termes IGN] orthoimage [Termes IGN] polyligne [Termes IGN] primitive géométrique [Termes IGN] relation topologique [Termes IGN] réseau routier [Termes IGN] segmentation sémantique [Termes IGN] squelettisation [Termes IGN] zone urbaine [Vedettes matières IGN] Généralisation Résumé : (auteur) Streets are essential entities of urban terrain and their automatic extraction from airborne sensor data is cumbersome because of a complex interplay of geometric, topological, and semantic aspects. Given a binary image representing the road class, centerlines of road segments are extracted by means of skeletonization. The focus of this paper lies in a well-reasoned representation of these segments by means of geometric primitives, such as straight line segments as well as circle and ellipse arcs. Thereby, we aim at a fusion of raw segments to longer chains which better match to the intuitive perception of what a street is. We propose a two-step approach for simultaneous chain-forming and generalization. First, we obtain an over-segmentation of the raw polylines. Then, a model selection approach is applied to decide whether two neighboring segments should be fused to a new geometric entity. For this purpose, we propose an iterative greedy optimization procedure in order to find a strong minimum of a cost function based on a Bayesian information criterion. Starting at the given initial raw segments, we thus can obtain a set of chains describing long alleys and important roundabouts. Within the procedure, topological attributes, such as junctions and neighborhood structures, are consistently updated, in a way that for the greedy optimization procedure, accuracy, model complexity, and topology are considered simultaneously. The results on two challenging datasets indicate the benefits of the proposed procedure and provide ideas for future work. Numéro de notice : A2019-026 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.19 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.19 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91962 [article]

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105-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Integration of lidar data and GIS data for point cloud semantic enrichment at the point level / Harith Aljumaily in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 29 - 42 Titre : Integration of lidar data and GIS data for point cloud semantic enrichment at the point level Type de document : Article/Communication Auteurs : Harith Aljumaily, Auteur ; Debra F. Laefer, Auteur ; Dolores Cuadra, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 42 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Dublin (Irlande ; ville) [Termes IGN] enrichissement sémantique [Termes IGN] extraction de la végétation [Termes IGN] extraction du réseau routier [Termes IGN] flore urbaine [Termes IGN] image multibande [Termes IGN] information sémantique [Termes IGN] interpolation linéaire [Termes IGN] OpenStreetMap [Termes IGN] réseau routier [Termes IGN] segmentation [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] système de gestion de base de données [Termes IGN] zone urbaine Résumé : (auteur) Commercial aerial laser scanning is generally delivered with point-by-point metadata for object identification, but current vendor-generated classification approaches (which rely exclusively on that data) generate high misclassification rates in urban areas. To overcome this problem and provide a fully scalable solution that harnesses distributed computing capabilities, this paper introduces a novel system, employing a MapReduce framework and existing GIS-based data, to provide more detailed and accurate classification. The approach goes beyond traditional gross-level classification (roads, buildings, trees, noise) by enriching the point cloud metadata with detailed semantic information about the object type. The approach was evaluated using two datasets of differing point density, separated by eight years for the same study area in Dublin, Ireland. As evaluated against manually classified data, classification quality ranged from 76% to 91% depending upon category and only 8% remained unclassified, as opposed to the commercial vendor's classification quality which ranged from 43% to 78% with 82% left unclassified. Numéro de notice : A2019-027 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.29 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.29 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91964 [article]

Exemplaires(1)

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105-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data / Long Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 43 - 54 Titre : Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data Type de document : Article/Communication Auteurs : Long Wang, Auteur ; Binbin He, Auteur ; Xiaojing Bai, Auteur ; Minfeng Xing, Auteur Année de publication : 2019 Article en page(s) : pp 43 - 54 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Enhanced vegetation index [Termes IGN] étalonnage de modèle [Termes IGN] humidité du sol [Termes IGN] image Sentinel-SAR [Termes IGN] image Terra-MODIS [Termes IGN] indice foliaire [Termes IGN] Iowa (Etats-Unis) [Termes IGN] Leaf Area Index [Termes IGN] modèle de rétrodiffusion [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] série temporelle Résumé : (auteur) Soil moisture is an important state variable of the land surface ecosystem. In this paper, the water cloud model (WCM) and advanced integral equation model (AIEM) are coupled to retrieve soil moisture using time series Sentinel-1A data and moderate resolution imaging spectroradiometer (MODIS) data. Normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR), are cross-combined to initialize the calibrated model. The calibration results show the following: (1) Vegetation parameters have a great influence on model calibration; and (2) The combination of (NDVI, LAI) is recommended to calibrate the coupled model, the RMSE, R2 is 0.739 dB, and 0.716 for the observed and estimated backscattering coefficients. The soil moisture inversion results show that: (1) the accuracy of model calibration and soil moisture inversion are inconsistent; and (2) The normalized vegetation parameters, such as NDVI, EVI and FPAR, are suitable for WCM to describe vegetation characteristics, and NDVI is the optimum. When V2 is the NDVI, the average bias, MAE, RMSE, ubRMSE and R2 are –0.007 m3/m3, 0.074 m3/m3, 0.087 m³/m³, 0.087 m3/m3 and 0.750, respectively. Numéro de notice : A2019-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.43 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.43 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91965 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible

Individual tree detection and crown delineation with 3D information from multi-view satellite Images / Changlin Xiao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 1 (January 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 1 (January 2019) . - pp 55 - 63 Titre : Individual tree detection and crown delineation with 3D information from multi-view satellite Images Type de document : Article/Communication Auteurs : Changlin Xiao, Auteur ; Rongjun Qin, Auteur ; Xiao Xie, Auteur ; Xu Huang, Auteur Année de publication : 2019 Article en page(s) : pp 55 - 63 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Buenos Aires (Argentine) [Termes IGN] CloudCompare [Termes IGN] densité de la végétation [Termes IGN] détection d'arbres [Termes IGN] données localisées 3D [Termes IGN] flore urbaine [Termes IGN] forêt [Termes IGN] hauteur des arbres [Termes IGN] houppier [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] image Worldview [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] modèle numérique de surface [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] segmentation d'image Résumé : (auteur) Individual tree detection and crown delineation (ITDD) are critical in forest inventory management and remote sensing based forest surveys are largely carried out through satellite images. However, most of these surveys only use 2D spectral information which normally has not enough clues for ITDD. To fully explore the satellite images, we propose a ITDD method using the orthophoto and digital surface model (DSM) derived from the multi-view satellite data. Our algorithm utilizes the top-hat morphological operation to efficiently extract the local maxima from DSM as treetops, and then feed them to a modified superpixel segmentation that combines both 2D and 3D information for tree crown delineation. In subsequent steps, our method incorporates the biological characteristics of the crowns through plant allometric equation to falsify potential outliers. Experiments against manually marked tree plots on three representative regions have demonstrated promising results – the best overall detection accuracy can be 89%. Numéro de notice : A2019-030 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.1.55 Date de publication en ligne : 01/01/2019 En ligne : https://doi.org/10.14358/PERS.85.1.55 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91966 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019011	SL	Revue	Centre de documentation	Revues en salle	Disponible