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Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)
Titre : Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Martin Weinmann, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Projets : 1-Pas de projet / Conférence : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 2768 - 2771 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme Graph-Cut
[Termes IGN] analyse comparative
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inférence
[Termes IGN] semis de points
[Termes IGN] test de performance
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) In this paper, we focus on the classification of lidar point cloud data acquired via mobile laser scanning, whereby the classification relies on a context model based on a Conditional Random Field (CRF). We present two approximate inference algorithms based on belief propagation, as well as a graph-cut-based approach not yet applied in this context. To demonstrate the performance of our approach, we present the classification results derived for a standard benchmark dataset. These results clearly indicate that the graph-cut-based method is able to retrieve a labeling of higher likelihood in only a fraction of the time needed for the other approaches. The higher likelihood, in turn, translates into a significant gain in the accuracy of the obtained classification. Numéro de notice : C2017-026 Affiliation des auteurs : IGN+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127571 Date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89289 Documents numériques
en open access
Comparison of belief propagation ... - postprintAdobe Acrobat PDF Computationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process / Xing Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)
[article]
Titre : Computationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process Type de document : Article/Communication Auteurs : Xing Sun, Auteur ; Nelson H. C. Yung, Auteur ; Edmund Y. Lam, Auteur ; Hayden K.-H. So, Auteur Année de publication : 2017 Article en page(s) : pp 363 - 374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] image hyperspectrale
[Termes IGN] modèle stochastique
[Termes IGN] problème de DirichletRésumé : (Auteur) The Dirichlet process (DP) prior is effective in modeling HSIs (HSI) and identifying land-cover classes. However, modeling a continuously varying intensity of these land covers elegantly and consistently is still a challenge. We propose a doubly stochastic DP (DSDP) as an efficient model of the global topic measurement space, which imposes a weaker assumption compared with the discrete Markov assumption, resulting in a lower computational cost than other DP-prior-based models. We also present a mixture model of DSDP, which is termed the marked sigmoidal Gaussian process (SGP) DSDP mixture model. It can be thinned from a DP mixture without massive auxiliary covariates, and the marked function prior makes the number of land-cover classes consistent, whereas the SGP function prior models the HSI land-cover variation globally. The consistency of the number of land covers is maintained for various HSIs with large-scale geographical areas. Experiments show that the model is robust and consistent on HSI identification with weak or even no supervision. Numéro de notice : A2017-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2606575 En ligne : https://doi.org/10.1109/TGRS.2016.2606575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83951
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 363 - 374[article]Exterior orientation revisited : a robust method based on lq -norm / Jiayuan Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 1 (January 2017)
[article]
Titre : Exterior orientation revisited : a robust method based on lq -norm Type de document : Article/Communication Auteurs : Jiayuan Li, Auteur ; Qingwu Hu, Auteur ; Ruofei Zhong, Auteur ; Mingyao Ai, Auteur Année de publication : 2017 Article en page(s) : pp 47 - 56 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] caméra numérique
[Termes IGN] équation de Lagrange
[Termes IGN] orientation du capteur
[Termes IGN] orientation externe
[Termes IGN] valeur aberranteRésumé : (Auteur) Camera exterior orientation is essential in many photogrammetry and computer vision applications, including 3D reconstruction, digital orthophoto map (DOM) generation, and localization. In this paper, we propose a new formulation of exterior orientation that is robust against gross errors (outliers). Different from classic optimization methods whose cost function is based on the l q -norm of residuals, we use l q -norm (0 Numéro de notice : A2017-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.1.47 En ligne : https://doi.org/10.14358/PERS.83.1.47 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84092
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 1 (January 2017) . - pp 47 - 56[article]Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)
Titre : Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas Type de document : Mémoire Auteurs : Cyril Wendl, Auteur ; Arnaud Le Bris , Encadrant Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2017 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de stage, Ecole Polytechnique Fédérale de Lausanne EPFLLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] estimation bayesienne
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation
[Termes IGN] théorie de Dempster-ShaferIndex. décimale : MASTX Mémoires de masters divers Résumé : (auteur) Fusion of very high spatial resolution multispectral images with lower spatial resolution image time series having a higher number of bands can improve land use classification, combining geometric and semantic advantages of both sources. This study presents a workflow to extract the extent of urbanized ground using decision-level fusion and regularization of individual classifications on Sentinel-2 and SPOT-6 satellite images. First, both images are classified individually in five classes, using state-of-the-art supervised classification approaches and Convolutional Neural Networks. Decision-level fusion and regularization are used to combine the spatial and spectral advantages of both sources: First, both sources are merged in order to extract building labels with as high semantic and spatial precision as possible. Second, the building labels are used together with the Sentinel-2 classification as input for a binary classification of the artificialized area; the building labels from the regularization are dilated in order to connect the building objects and a binary classification is derived from the original Sentinel-2 classification before these two separate binary classifications are reintroduced in a fusion and regularization to find the artificialized area. Segmentation of the Sentinel-2 satellite image and majority voting of the object-level classification are also used to refine the contours of the artificialized area. Note de contenu : Introduction
1 - Methodology
2 - Artificialized area
3 - Results
ConclusionNuméro de notice : 21702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Rapport de stage Organisme de stage : MATIS (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90951 Documents numériques
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Fusion of Multi-Temporal... - pdf auteur -Adobe Acrobat PDF GPS/BDS short-term ISB modelling and prediction / Nan Jiang in GPS solutions, vol 21 n° 1 (January 2017)
[article]
Titre : GPS/BDS short-term ISB modelling and prediction Type de document : Article/Communication Auteurs : Nan Jiang, Auteur ; Yan Xu, Auteur ; Tianhe Xu, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 163 - 175 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] analyse comparative
[Termes IGN] données BeiDou
[Termes IGN] données GNSS
[Termes IGN] erreur systématique
[Termes IGN] erreur systématique inter-systèmes
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle d'erreur
[Termes IGN] orbitographie
[Termes IGN] positionnement par BeiDou
[Termes IGN] positionnement par GNSS
[Termes IGN] positionnement ponctuel précisRésumé : (Auteur) The Chinese BeiDou Navigation Satellite System (BDS) has completed its first milestone by providing coverage of the Asia–Pacific area navigation service since December 27, 2012. With the combination of BDS, the GNSS precise point positioning (PPP) can improve its positioning accuracy, availability and reliability. However, in order to achieve the best positioning solutions, the inter-system bias (ISB) between GPS and BDS must be resolved as precisely as possible. In this study, a 1-week period (GPS week 1810) of GPS/BDS observations for 18 distributed stations from the International GNSS Service Multi-GNSS Experiment are processed. Primarily, the ISB is estimated by an extended Kalman filter as a piece-wise parameter every 30 min. Then we generate a smoothed ISB series (ISB_s) with a sliding window median filter to reject the outliers from the original estimated ISB series (ISB_o). After analysing the characteristics of the ISB_s, a short-term station-dependent ISB model based on a 1-week period is proposed in this study. This model consists of a quadratic polynomial in time and two or three periodic functions with diurnal and semi-diurnal periods. Frequency spectrum analysis is used to determine the periods of the periodic functions, and the coefficients of the quadratic function and the periodic functions are estimated by least squares. For model verification, we compare the ISB derived from the model (ISB_m) with ISB_s (assumed the true values). The comparisons indicate an almost normal distribution. It is found that the proposed model is consistent with the true values: the root-mean-square (RMS) values being about 0.7 ns, and some stations are even better. This means that the short-term ISB model proposed has a high fitting accuracy. Hence, it can be used for ISB prediction. Comparing the prediction ISB series (ISB_p) with ISB_s in the following week (GPS week 1811), we can draw the conclusion that the accuracy of the prediction declines with an increase in the time period. The 1-day period precision can achieve 0.57–1.21 ns, while the accuracy of the 2-day prediction decreases to 0.77–1.72 ns. Hence, we recommend a predicting duration of 1 day. The proposed model will be beneficial for subsequent GPS/BDS PPP or precise orbit determination (POD) since the ISB derived from this model can be considered as a priori constraint in the PPP/POD solutions. With this a priori constraint, the convergence time can be shortened by 19.6, 16.1 and 2.4 % in N, E and U components, respectively. The accuracy of result in the E component is remarkably improved by 11.9 %. Numéro de notice : A2017-015 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-015-0513-x Date de publication en ligne : 09/01/2016 En ligne : https://doi.org/10.1007/s10291-015-0513-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83946
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