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extraction de traits caractéristiquesSynonyme(s)extraction des caractéristiques extraction de primitiveVoir aussi |
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A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery / Zewei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
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Titre : A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery Type de document : Article/Communication Auteurs : Zewei Xu, Auteur ; Kaiyu Guan, Auteur ; Nathan Casler, Auteur ; Bin Peng, Auteur ; Shaowen Wang, Auteur Année de publication : 2018 Article en page(s) : pp 423 - 434 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Illinois (Etats-Unis)
[Termes IGN] image Landsat
[Termes IGN] image multitemporelle
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Terrestrial landscape has complex three-dimensional (3D) features that are difficult to extract using traditional methods based on 2D representations. These methods often relegate such features to raster or metric-based (two-dimensional) representations based on Digital Surface Models (DSM) or Digital Elevation Models (DEM), and thus are not suitable for resolving morphological and intensity features for fine-scale land cover mapping. Small-footprint LiDAR provides an ideal way for capturing these 3D features. This research develops a novel method of integrating airborne LiDAR derived features and multi-temporal Landsat images to classify land cover types. We tested our approach in Williamson County, Illinois, which has diverse and mixed landscape features. Specifically, our method applied a 3D convolutional neural network (CNN) approach to extract features from LiDAR point clouds by (1) creating an occupancy grid, an intensity grid at 1-meter resolution, and then (2) normalizing and incorporating data into the 3D CNN. The extracted features (e.g., morphological and intensity features) from the 3D CNN were finally combined with multi-temporal spectral data to enhance the performance of land cover classification based on a Support Vector Machine classifier. Visual interpretation from both hyper-resolution photos and point clouds was used for training and preparation of testing data. The classification results show that our method outperforms a traditional method by 2.65% (from 81.52% to 84.17%) when solely using LiDAR and 2.19% (from 90.20% to 92.57%) when combining all available imageries. We demonstrate that our method can effectively extract LiDAR features and improve fine-scale land cover mapping through fusion of complementary types of remote sensing data. Numéro de notice : A2018-405 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.08.005 Date de publication en ligne : 22/08/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90859
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 423 - 434[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)
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Titre : Automated extraction of 3D vector topographic feature line from terrain point cloud Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Rencan Peng, Auteur ; Jian Dong, Auteur ; Tao Wang, Auteur Année de publication : 2018 Article en page(s) : pp 1036 - 1047 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre aléatoire minimum
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] ligne caractéristique
[Termes IGN] lissage de données
[Termes IGN] modèle numérique de terrain
[Termes IGN] objet géographique linéaire
[Termes IGN] repère de Laplace
[Termes IGN] segmentation en régions
[Termes IGN] semis de pointsRésumé : (auteur) This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm. Numéro de notice : A2019-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1325521 Date de publication en ligne : 18/05/2017 En ligne : https://doi.org/10.1080/10106049.2017.1325521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92064
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1036 - 1047[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 / Umut Gunes Sefercik in Geocarto international, vol 33 n° 10 (October 2018)
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Titre : Novel fusion approach on automatic object extraction from spatial data: case study Worldview-2 and TOPO5000 Type de document : Article/Communication Auteurs : Umut Gunes Sefercik, Auteur ; Serkan Karakis, Auteur ; Can Atalay, Auteur ; Ibrahim Yigit, Auteur ; Umit Gokmen, Auteur Année de publication : 2018 Article en page(s) : pp 1139 - 1154 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] détection d'objet
[Termes IGN] détection du bâti
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction du réseau routier
[Termes IGN] filtre de Wallis
[Termes IGN] image numérique
[Termes IGN] image Worldview
[Termes IGN] modèle numérique de surface
[Termes IGN] TurquieRésumé : (auteur) The automatic extraction of information content from remotely sensed data is always challenging. We suggest a novel fusion approach to improve the extraction of this information from mono-satellite images. A Worldview-2 (WV-2) pan-sharpened image and a 1/5000-scaled topographic vector map (TOPO5000) were used as the sample data. Firstly, the buildings and roads were manually extracted from WV-2 to point out the maximum extractable information content. Subsequently, object-based automatic extractions were performed. After achieving two-dimensional results, a normalized digital surface model (nDSM) was generated from the underlying digital aerial photos of TOPO5000, and the automatic extraction was repeated by fusion with the nDSM to include individual object heights as an additional band for classification. The contribution was tested by precision, completeness and overall quality. Novel fusion technique increased the success of automatic extraction by 7% for the number of buildings and by 23% for the length of roads. Numéro de notice : A2019-047 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1353646 Date de publication en ligne : 27/07/2017 En ligne : https://doi.org/10.1080/10106049.2017.1353646 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92068
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1139 - 1154[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier / Huanxue Zhang in Geocarto international, vol 33 n° 10 (October 2018)
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Titre : Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier Type de document : Article/Communication Auteurs : Huanxue Zhang, Auteur ; Qiangzi Li, Auteur ; Jiangui Liu, Auteur ; Taifeng Dong, Auteur ; Heather McNairn, Auteur Année de publication : 2018 Article en page(s) : pp 1017 - 1035 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] bande spectrale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] corrélation par régions de niveaux de gris
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image SPOT 5
[Termes IGN] indice de végétation
[Termes IGN] limite de terrain
[Termes IGN] Ontario (Canada)
[Termes IGN] réflectance spectrale
[Termes IGN] segmentation d'image
[Termes IGN] surface cultivée
[Termes IGN] surveillance agricole
[Termes IGN] texture d'image
[Termes IGN] variogrammeRésumé : (auteur) In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification. Numéro de notice : A2019-049 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1333533 Date de publication en ligne : 23/06/2017 En ligne : https://doi.org/10.1080/10106049.2017.1333533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92063
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1017 - 1035[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Extraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)
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Titre : Extraction of building roof planes with stratified random sample consensus Type de document : Article/Communication Auteurs : André C. Carrilho, Auteur ; Mauricio Galo, Auteur Année de publication : 2018 Article en page(s) : pp 363 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] morphologie mathématique
[Termes IGN] Ransac (algorithme)
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] varianceRésumé : (Auteur) This paper describes a consensus‐set estimation for building roof‐plane detection using a stratified random sample consensus (sRANSAC) algorithm applied to point clouds acquired by laser scanning systems. The main idea is to use one initial classification to generate consensus‐set candidates to optimise the sampling mechanism compared to the original RANSAC. The initial classification is performed using mathematical morphology to filter ground returns and estimate local variance information to detect potential planar regions. Thus, the algorithm can prioritise points within planar segments and the number of iterations can be estimated dynamically from available data. The results based on experiments using five different lidar datasets indicate that the proposed method reduces the number of computations for building roof‐plane detection and also improves accuracy compared to RANSAC. Numéro de notice : A2018-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12254 Date de publication en ligne : 21/09/2018 En ligne : https://doi.org/10.1111/phor.12254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92864
in Photogrammetric record > vol 33 n° 163 (September 2018) . - pp 363 - 380[article]Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)
PermalinkThree-dimensional building façade segmentation and opening area detection from point clouds / S.M. Iman Zolanvari in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)
PermalinkExploring uncertainties in terrain feature extraction across multi-scale, multi-feature, and multi-method approaches for variable terrain / Boleslo E. Romero in Cartography and Geographic Information Science, Vol 45 n° 5 (August 2018)
PermalinkA fully automatic approach to register mobile mapping and airborne imagery to support the correction of plateform trajectories in GNSS-denied urban areas / Phillipp Jende in ISPRS Journal of photogrammetry and remote sensing, vol 141 (July 2018)
PermalinkFusion tardive d’images SPOT 6/7 et de données multitemporelles Sentinel-2 pour la détection de la tache urbaine / Cyril Wendl in Revue Française de Photogrammétrie et de Télédétection, n° 217-218 (juin - septembre 2018)
PermalinkAccurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information / Yongzhi Wang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)
PermalinkGenerative street addresses from satellite imagery / İlke Demir in ISPRS International journal of geo-information, vol 7 n° 3 (March 2018)
PermalinkMultisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)
PermalinkAn (almost) automated process to track the Martians dunes : ac.GetPreciseShifts / Arthur Coqué (2018)
PermalinkAutomated extraction of hydrographically corrected contours for the conterminous United States: the US Geological Survey US Topo product / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 45 n° 1 (January 2018)
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