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Building extraction from fused LiDAR and hyperspectral data using Random Forest Algorithm / Saeid Parsian in Geomatica, vol 71 n° 4 (December 2017)
[article]
Titre : Building extraction from fused LiDAR and hyperspectral data using Random Forest Algorithm Type de document : Article/Communication Auteurs : Saeid Parsian, Auteur ; Meisam Amani, Auteur Année de publication : 2017 Article en page(s) : pp 185 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse discriminante
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image hyperspectrale
[Termes IGN] morphologie mathématique
[Termes IGN] toitRésumé : (Auteur) Dans le présent article, nous avons utilisé la fusion des données de la détection et de la télémétrie par ondes lumineuses (LiDAR) avec les données hyperspectrales afin de proposer une méthode pour la détection des édifices. Le nombre de bandes hyperspectrales a d’abord été réduit de 144 couches à 8 couches en utilisant l’algorithme d’analyse discriminante linéaire (ADL) pour enlever les bandes hautement redondantes et réduire les coûts de calcul. Puis, ces couches ont été intégrées à quatre couches de hauteurs et d’intensités obtenues des données LiDAR. Les couches fusionnées (12 couches) ont été appliquées à un algorithme de forêts aléatoires (FA) pour extraire les limites des édifices. Finalement, deux opérateurs morphologiques ont été appliqués pour enlever les ouvertures dans les toits des édifices et réparer leurs limites. Une comparaison a également été effectuée entre les résultats obtenus au moyen de la méthode proposée et l’étude de référence dans ce domaine [Debes et al. 2014]. La précision de la méthode proposée s’est avérée meilleure pour la détection des édifices en beaucoup moins de temps comparativement à la méthode de référence. Les valeurs de 97 % et de 96 % ont été obtenues pour la précision du producteur et de l’utilisateur respectivement. Dans l’ensemble, la méthode décrite dans le présent article s’est avérée avoir un potentiel élevé pour l’extraction des édifices. Numéro de notice : A2017-848 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.5623/cig2017-401 Date de publication en ligne : 27/02/2018 En ligne : https://doi.org/10.5623/cig2017-401 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89446
in Geomatica > vol 71 n° 4 (December 2017) . - pp 185 - 193[article]Centrality-based hierarchy for street network generalization in multi-resolution maps / Wasim Shoman in Geocarto international, vol 32 n° 12 (December 2017)
[article]
Titre : Centrality-based hierarchy for street network generalization in multi-resolution maps Type de document : Article/Communication Auteurs : Wasim Shoman, Auteur ; Fatih Gülgen, Auteur Année de publication : 2017 Article en page(s) : pp 1352 - 1366 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] axe médian
[Termes IGN] base de données multi-représentation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] généralisation automatique de données
[Termes IGN] graphe
[Termes IGN] processus de hiérarchisation analytique floue
[Termes IGN] réseau routier
[Termes IGN] route
[Termes IGN] zoom
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) This paper introduces a new hierarchy for cartographic generalisation processes, applied in street networks. The aims of implementing this hierarchy are to emphasise on significant street features, and to provide more free spaces between street features. The hierarchy is obtained from the functional classes of the features and four centrality measures in a street network, i.e. betweenness, reach, straightness and closeness extracted from a primary graph. The values of centrality measures change in every zoom level by calculating a radius parameter, which depends on the users’ field of view. The coefficients for the measures are constructed using a decision-making technique called fuzzy analytical hierarchy process (FAHP). The weights for each of the centrality measures are computed and normalised to form the proposed hierarchy. The hierarchy is applied and used later in the thinning process to omit insignificant features from the street network in medium scales. Numéro de notice : A2017-673 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1208683 En ligne : https://doi.org/10.1080/10106049.2016.1208683 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87164
in Geocarto international > vol 32 n° 12 (December 2017) . - pp 1352 - 1366[article]DEM generation from contours and a low-resolution DEM / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)
[article]
Titre : DEM generation from contours and a low-resolution DEM Type de document : Article/Communication Auteurs : Xinghua Li, Auteur ; Huanfeng Shen, Auteur ; Ruitao Feng, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 135 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] détection de contours
[Termes IGN] krigeage
[Termes IGN] MNS ASTER
[Termes IGN] MNS SRTM
[Termes IGN] modèle numérique de surface
[Termes IGN] programmation par contraintes
[Termes IGN] régularisation
[Termes IGN] représentation discrèteRésumé : (Auteur) A digital elevation model (DEM) is a virtual representation of topography, where the terrain is established by the three-dimensional co-ordinates. In the framework of sparse representation, this paper investigates DEM generation from contours. Since contours are usually sparsely distributed and closely related in space, sparse spatial regularization (SSR) is enforced on them. In order to make up for the lack of spatial information, another lower spatial resolution DEM from the same geographical area is introduced. In this way, the sparse representation implements the spatial constraints in the contours and extracts the complementary information from the auxiliary DEM. Furthermore, the proposed method integrates the advantage of the unbiased estimation of kriging. For brevity, the proposed method is called the kriging and sparse spatial regularization (KSSR) method. The performance of the proposed KSSR method is demonstrated by experiments in Shuttle Radar Topography Mission (SRTM) 30 m DEM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m global digital elevation model (GDEM) generation from the corresponding contours and a 90 m DEM. The experiments confirm that the proposed KSSR method outperforms the traditional kriging and SSR methods, and it can be successfully used for DEM generation from contours. Numéro de notice : A2017-735 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.09.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.09.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88432
in ISPRS Journal of photogrammetry and remote sensing > vol 134 (December 2017) . - pp 135 - 147[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017122 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017123 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network / Wei Zhao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)
[article]
Titre : Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network Type de document : Article/Communication Auteurs : Wei Zhao, Auteur ; Zhirui Wang, Auteur ; Maoguo Gong, Auteur ; Jia Liu, Auteur Année de publication : 2017 Article en page(s) : pp 7066 - 7080 Note générale : Bibliograpie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection de changementRésumé : (Auteur) With the application requirement, the technique for change detection based on heterogeneous remote sensing images is paid more attention. However, detecting changes between two heterogeneous images is challenging as they cannot be compared in low-dimensional space. In this paper, we construct an approximately symmetric deep neural network with two sides containing the same number of coupled layers to transform the two images into the same feature space. The two images are connected with the two sides and transformed into the same feature space, in which their features are more discriminative and the difference image can be generated by comparing paired features pixel by pixel. The network is first built by stacked restricted Boltzmann machines, and then, the parameters are updated in a special way based on clustering. The special way, motivated by that two heterogeneous images share the same reality in unchanged areas and retain respective properties in changed areas, shrinks the distance between paired features transformed from unchanged positions, and enlarges the distance between paired features extracted from changed positions. It is achieved through introducing two types of labels and updating parameters by adaptively changed learning rate. This is different from the existing methods based on deep learning that just do operations on positions predicted to be unchanged and extract only one type of labels. The whole process is completely unsupervised without any priori knowledge. Besides, the method can also be applied to homogeneous images. We test our method on heterogeneous images and homogeneous images. The proposed method achieves quite high accuracy. Numéro de notice : A2017-768 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2739800 En ligne : https://doi.org/10.1109/TGRS.2017.2739800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88807
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 7066 - 7080[article]Learning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)
[article]
Titre : Learning aggregated features and optimizing model for semantic labeling Type de document : Article/Communication Auteurs : Jianhua Wang, Auteur ; Chuanxia Zheng, Auteur ; Weihai Chen, Auteur ; Xingming Wu, Auteur Année de publication : 2017 Article en page(s) : pp 1587 - 1600 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] attribut
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image RVB
[Termes IGN] modèle statistique
[Termes IGN] scène intérieure
[Termes IGN] segmentation d'imageRésumé : (Auteur) Semantic labeling for indoor scenes has been extensively developed with the wide availability of affordable RGB-D sensors. However, it is still a challenging task for multi-class recognition, especially for “small” objects. In this paper, a novel semantic labeling model based on aggregated features and contextual information is proposed. Given an RGB-D image, the proposed model first creates a hierarchical segmentation using an adapted gPb/UCM algorithm. Then, a support vector machine is trained to predict initial labels using aggregated features, which fuse small-scale appearance features, mid-scale geometric features, and large-scale scene features. Finally, a joint multi-label Conditional random field model that exploits both spatial and attributive contextual relations is constructed to optimize the initial semantic and attributive predicted results. The experimental results on the public NYU v2 dataset demonstrate the proposed model outperforms the existing state-of-the-art methods on the challenging 40 dominant classes task, and the model also achieves a good performance on a recent SUN RGB-D dataset. Especially, the prediction accuracy of “small” classes has been improved significantly. Numéro de notice : A2017-714 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1302-4 En ligne : https://doi.org/10.1007/s00371-016-1302-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88098
in The Visual Computer > vol 33 n° 12 (December 2017) . - pp 1587 - 1600[article]Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification / Yanqiao Chen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkMultimorphological superpixel model for hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkSingle image dehazing via an improved atmospheric scattering model / Mingye Ju in The Visual Computer, vol 33 n° 12 (December 2017)PermalinkThorough statistical comparison of machine learning regression models and their ensembles for sub-pixel imperviousness and imperviousness change mapping / Wojciech Drzewiecki in Geodesy and cartography, vol 66 n° 2 (December 2017)PermalinkExtraction du bâti sur le territoire de la wilaya de Blida (Algérie) / Siham Bougdour in Géomatique expert, n° 119 (novembre - décembre 2017)PermalinkRobust minimum volume simplex analysis for hyperspectral unmixing / Shaoquan Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkSalient object detection in complex scenes via D-S evidence theory based region classification / Chunlei Yang in The Visual Computer, vol 33 n° 11 (November 2017)PermalinkSparse distributed multitemporal hyperspectral unmixing / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkSpatial group sparsity regularized nonnegative matrix factorization for hyperspectral unmixing / Xinyu Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkTree species classification using within crown localization of waveform LiDAR attributes / Rosmarie Blomley in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)PermalinkAutomatic shadow detection in aerial and terrestrial images / Vander Luis de Souza Freitas in Boletim de Ciências Geodésicas, vol 23 n° 4 (oct - dec 2017)PermalinkRegistration of images to Lidar and GIS data without establishing explicit correspondences / Gabor Barsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)PermalinkTowards a multi-scale approach for an Earth observation-based assessment of natural resource exploitation in conflict regions / Elisabeth Schoepfer in Geocarto international, vol 32 n° 10 (October 2017)PermalinkOccupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)PermalinkDenoising of natural images through robust wavelet thresholding and genetic programming / Asem Khmag in The Visual Computer, vol 33 n°9 (September 2017)PermalinkFacet segmentation-based line segment extraction for large-scale point clouds / Yangbin Lin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkForest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkMultiple cues-based active contours for target contour tracking under sophisticated background / Peng Lv in The Visual Computer, vol 33 n°9 (September 2017)PermalinkSpatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements / Zhibin Ren in Annals of Forest Science, vol 74 n° 3 (September 2017)PermalinkThe geometry of space-time prisms with uncertain anchors / Bart Kuijpers in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)PermalinkUrban building reconstruction from raw LiDAR point data / Cheng Yi in Computer-Aided Design, vol 9x (2017)PermalinkSentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping / Jan Haas in Remote Sensing Applications: Society and Environment, RSASE, vol 8 (November 2017)PermalinkLocal Moebius transformations applied to omnidirectional images / Leonardo Souto Ferreira in Computers and graphics, vol 68 (November 2017)Permalink3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkChange detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications / Zhe Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkA graph-based approach to detect spatiotemporal dynamics in satellite image time series / Fabio Guttler in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkJoint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkLocal and global evaluation for remote sensing image segmentation / Tengfei Su in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkA novel preunmixing framework for efficient detection of linear mixtures in hyperspectral images / Andrea Marinoni in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkReducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkRobust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkSimultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks / Rasha Alshehhi in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)PermalinkStructure from motion with line segments under relaxed endpoint constraints / Branislav Micusik in International journal of computer vision, vol 124 n° 1 (August 2017)PermalinkA TV prior for high-quality scalable multi-view stereo reconstruction / Andreas Kuhn in International journal of computer vision, vol 124 n° 1 (August 2017)PermalinkFusion of Landsat 8 OLI and sentinel-2 MSI data / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkInteractive shearing for terrain visualization : an expert study / Jonas Buddeberg in Geoinformatica, vol 21 n° 3 (July - September 2017)PermalinkJoint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)PermalinkA novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkTotal variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing / Wei He in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkAutomatic illumination-invariant image-to-geometry registration in outdoor environments / Christian Kehl in Photogrammetric record, vol 32 n° 158 (June - july 2017)PermalinkChange detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkChange detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)Permalink