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An interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)
[article]
Titre : An interactive tool for semi-automatic feature extraction of hyperspectral data Type de document : Article/Communication Auteurs : Zoltan Kovacs, Auteur ; Szilárd Szabó, Auteur Année de publication : 2016 Article en page(s) : pp 493 - 502 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] extraction semi-automatique
[Termes IGN] image hyperspectrale
[Termes IGN] régression
[Termes IGN] spectrométrie
[Termes IGN] VBARésumé : (auteur) The spectral reflectance of the surface provides valuable information about the environment, which can be used to identify objects (e.g. land cover classification) or to estimate quantities of substances (e.g. biomass). We aimed to develop an MS Excel add-in – Hyperspectral Data Analyst (HypDA) – for a multipurpose quantitative analysis of spectral data in VBA programming language. HypDA was designed to calculate spectral indices from spectral data with user defined formulas (in all possible combinations involving a maximum of 4 bands) and to find the best correlations between the quantitative attribute data of the same object. Different types of regression models reveal the relationships, and the best results are saved in a worksheet. Qualitative variables can also be involved in the analysis carried out with separability and hypothesis testing; i.e. to find the wavelengths responsible for separating data into predefined groups. HypDA can be used both with hyperspectral imagery and spectrometer measurements. This bivariate approach requires significantly fewer observations than popular multivariate methods; it can therefore be applied to a wide range of research areas. Numéro de notice : A2016--071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/geo-2016-0040 En ligne : https://doi.org/10.1515/geo-2016-0040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84417
in Open geosciences > vol 8 n° 1 (January - July 2016) . - pp 493 - 502[article]Comparison of quality measures for building outline extraction / Markéta Potůčková in Photogrammetric record, vol 31 n° 154 (June - August 2016)
[article]
Titre : Comparison of quality measures for building outline extraction Type de document : Article/Communication Auteurs : Markéta Potůčková, Auteur ; Peter Hofman, Auteur Année de publication : 2016 Article en page(s) : pp 193 - 209 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection automatique
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] estimation de précision
[Termes IGN] lasergrammétrie
[Termes IGN] qualité des donnéesRésumé : (auteur) To date, numerous automatic building detection methods using lidar data have been developed. However, in most cases the parameters used for the evaluation of the results are not specified, accuracy assessment results cannot be readily compared or no result evaluation is performed at all. In this study, accuracy assessment aspects have been analysed in order to find a generally applicable approach for the comparison of building outline extraction methods. An area-based evaluation of accuracy has been selected as the most suitable due to its robustness, predictability and comparability between datasets. This assessment can be further supplemented with both object- and area-based evaluations, procured solely with those buildings that were classified as true positives, in order to interpret the extraction results in more detail. In cases where a reference dataset of superior quality (such as low-altitude aerial imagery) is available, an absolute accuracy evaluation is also relevant. Numéro de notice : A2016-468 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12144 Date de publication en ligne : 17/06/2016 En ligne : https://doi.org/10.1111/phor.12144 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81474
in Photogrammetric record > vol 31 n° 154 (June - August 2016) . - pp 193 - 209[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-2016021 RAB Revue Centre de documentation En réserve L003 Disponible A multilevel point-cluster-based discriminative feature for ALS point cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
[article]
Titre : A multilevel point-cluster-based discriminative feature for ALS point cloud classification Type de document : Article/Communication Auteurs : Zhenxin Zhang, Auteur ; Liqiang Zhang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3309 - 3321 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification automatique
[Termes IGN] codage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] semis de points
[Termes IGN] séparateur à vaste marge
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects. Numéro de notice : A2016-851 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2514508 En ligne : https://doi.org/10.1109/TGRS.2016.2514508 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82983
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3309 - 3321[article]Active-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)
[article]
Titre : Active-metric learning for classification of remotely sensed hyperspectral images Type de document : Article/Communication Auteurs : Edoardo Pasolli, Auteur ; Hsiuhan Lexie Yang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2016 Article en page(s) : pp 1925 - 1939 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiquesRésumé : (Auteur) Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method in which the feature extraction and AL steps are combined into a unique framework. The idea is to learn and update a reduced feature space in a supervised way at each iteration of the AL process, thus taking advantage of the increasing labeled information provided by the user. In particular, the computation of the reduced feature space is based on the large-margin nearest neighbor (LMNN) metric learning principle. This strategy is applied in conjunction with k-nearest neighbor ( k-NN) classification, for which a new sample selection strategy is proposed. The methodology is validated experimentally on four benchmark hyperspectral data sets. Good improvements in terms of classification accuracy and computational time are achieved with respect to the state-of-the-art strategies that do not combine feature extraction and AL. Numéro de notice : A2016-836 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2490482 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2490482 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82880
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 1925 - 1939[article]Street-side vehicle detection, classification and change detection using mobile laser scanning data / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 114 (April 2016)
[article]
Titre : Street-side vehicle detection, classification and change detection using mobile laser scanning data Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Konrad Schindler, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2016 Projets : Terra Mobilita / Article en page(s) : pp 166 - 178 Note générale : bibliogaphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle numérique d'objet
[Termes IGN] rectangle englobant minimum
[Termes IGN] semis de points
[Termes IGN] véhicule automobileRésumé : (auteur) Statistics on street-side car parks, e.g. occupancy rates, parked vehicle types, parking durations, are of great importance for urban planning and policy making. Related studies, e.g. vehicle detection and classification, mostly focus on static images or video. Whereas mobile laser scanning (MLS) systems are increasingly utilized for urban street environment perception due to their direct 3D information acquisition, high accuracy and movability. In this paper, we design a complete system for car park monitoring, including vehicle recognition, localization, classification and change detection, from laser scanning point clouds. The experimental data are acquired by an MLS system using high frequency laser scanner which scans the streets vertically along the system’s moving trajectory. The point clouds are firstly classified as ground, building façade, and street objects which are then segmented using state-of-the-art methods. Each segment is treated as an object hypothesis, and its geometric features are extracted. Moreover, a deformable vehicle model is fitted to each object. By fitting an explicit model to the vehicle points, detailed information, such as precise position and orientation, can be obtained. The model parameters are also treated as vehicle features. Together with the geometric features, they are applied to a supervised learning procedure for vehicle or non-vehicle recognition. The classes of detected vehicles are also investigated. Whether vehicles have changed across two datasets acquired at different times is detected to estimate the durations. Here, vehicles are trained pair-wisely. Two same or different vehicles are paired up as training samples. As a result, the vehicle recognition, classification and change detection accuracies are 95.9%, 86.0% and 98.7%, respectively. Vehicle modelling improves not only the recognition rate, but also the localization precision compared to bounding boxes. Numéro de notice : A2016--090 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.02.007 Date de publication en ligne : 03/03/2016 En ligne : http://doi.org/10.1016/j.isprsjprs.2016.02.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84612
in ISPRS Journal of photogrammetry and remote sensing > vol 114 (April 2016) . - pp 166 - 178[article]A feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)PermalinkLarge-scale road detection in forested mountainous areas using airborne topographic lidar data / António Ferraz in ISPRS Journal of photogrammetry and remote sensing, vol 112 (February 2016)PermalinkMatrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)PermalinkMulti-criteria, graph-based road centerline vectorization using ordered weighted averaging operators / Fateme Ameri in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkA region-line primitive association framework for object-based remote sensing image analysis / Wang Min in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkSeamline determination for high resolution orthoimage mosaicking using watershed segmentation / Wang Mi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)PermalinkPermalinkObject-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection / Anandakumar M. Ramiya in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)PermalinkRoad vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)PermalinkAn automated method to register airborne and terrestrial laser scanning point clouds / Bisheng Yang in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)PermalinkFusion of waveform LiDAR data and hyperspectral imagery for land cover classification / Hongzhou Wang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkA geometric method for wood-leaf separation using terrestrial and simulated Lidar data / Shengli Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkTriangulating social multimedia content for event localization using Flickr and Twitter / George Panteras in Transactions in GIS, vol 19 n° 5 (October 2015)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkAccurate affine invariant image matching using oriented least square / Amin Sedaghat in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 9 (September 2015)PermalinkMeasuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery / X. Chen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkRegistration of aerial imagery and lidar data in desert areas using sand ridges / Na Li in Photogrammetric record, vol 30 n° 151 (September - November 2015)PermalinkFull-waveform data for building roof step edge localization / Małgorzata Słota in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)PermalinkAutomatic transformation of different levels of detail in 3D GIS city models in CityGML / Yichuan Deng in International journal of 3-D information modeling, vol 4 n° 3 (July - September 2015)PermalinkCartographie du châtaignier en Alsace par imagerie satellite multi-date / Colette Meyer in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)PermalinkDétection à haute résolution spatiale de la desserte forestière en milieu montagneux / António Ferraz in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)PermalinkLocal binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkMulticlass feature learning for hyperspectral image classification: Sparse and hierarchical solutions / Devis Tuia in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)PermalinkOperationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)PermalinkSemantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers / Martin Weinmann in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)PermalinkFast forward feature selection of hyperspectral images for classification with gaussian mixture models / Mathieu Fauvel in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8 n° 6 (June 2015)PermalinkA fuzzy spatial reasoner for multi-scale GEOBIA ontologies / Argyros Argyridis in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)PermalinkA graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data / Victor F. Strimbu in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)PermalinkVery high resolution image matching based on local features and k-means clustering / Amin Sedaghat in Photogrammetric record, vol 30 n° 150 (June - August 2015)PermalinkMany eyes make light work / Simon Chester in Position, n° 76 (April - May 2015)PermalinkA multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkExtraction des éléments de façade de bâtiments du patrimoine architectural à partir de données issues de scanner laser terrestre / Kenza Aitelkadi in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)PermalinkSemiautomated extraction of street light poles from mobile LiDAR point-clouds / Yongtao Yu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkCoregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information / Janja Avbelj in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)PermalinkGabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkModélisation 3D de la chapelle Saint-Laurent et de la place du Château (secteur 3) pour extraction de données archéologiques et visite virtuelle / Robin Bruna (2015)PermalinkPermalinkA Random Forest class memberships based wrapper band selection criterion : application to hyperspectral / Arnaud Le Bris (2015)PermalinkSegmentation semi-automatique pour le traitement de données 3D denses : application au patrimoine architectural / Florent Poux in XYZ, n° 141 (décembre 2014 - février 2015)PermalinkAccuracy test of point-based and object-based urban building feature classification and extraction applying airborne LiDAR data / T. Tang in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)PermalinkAutomatic building extraction using a fuzzy active contour model / Mostafa Kabolizade in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)PermalinkA discriminative metric learning based anomaly detection method / Bo Du in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)PermalinkA new sparse source separation-based classification approach / M.A. Loghmari in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)PermalinkTraitement d’images satellitaires à très haute résolution spatiale et identification de zones à enjeux dans l’aménagement des Trames Vertes urbaines / Pauline Crombette in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)Permalink