Centre de documentation
scientifique

Dépouillements

Evaluation of feature-based 3-d registration of probabilistic volumetric scenes / Maria I. Restrepo in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 98 (December 2014) . - pp 1 - 18 Titre : Evaluation of feature-based 3-d registration of probabilistic volumetric scenes Type de document : Article/Communication Auteurs : Maria I. Restrepo, Auteur ; Ali O. Ulusoy, Auteur ; Joseph L. Mundy, Auteur Année de publication : 2014 Article en page(s) : pp 1 - 18 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] image aérienne [Termes IGN] modèle stochastique [Termes IGN] modélisation 3D [Termes IGN] superposition d'images Résumé : (Auteur) Automatic estimation of the world surfaces from aerial images has seen much attention and progress in recent years. Among current modeling technologies, probabilistic volumetric models (PVMs) have evolved as an alternative representation that can learn geometry and appearance in a dense and probabilistic manner. Recent progress, in terms of storage and speed, achieved in the area of volumetric modeling, opens the opportunity to develop new frameworks that make use of the PVM to pursue the ultimate goal of creating an entire map of the earth, where one can reason about the semantics and dynamics of the 3-d world. Aligning 3-d models collected at different time-instances constitutes an important step for successful fusion of large spatio-temporal information. This paper evaluates how effectively probabilistic volumetric models can be aligned using robust feature-matching techniques, while considering different scenarios that reflect the kind of variability observed across aerial video collections from different time instances. More precisely, this work investigates variability in terms of discretization, resolution and sampling density, errors in the camera orientation, and changes in illumination and geographic characteristics. All results are given for large-scale, outdoor sites. In order to facilitate the comparison of the registration performance of PVMs to that of other 3-d reconstruction techniques, the registration pipeline is also carried out using Patch-based Multi-View Stereo (PMVS) algorithm. Registration performance is similar for scenes that have favorable geometry and the appearance characteristics necessary for high quality reconstruction. In scenes containing trees, such as a park, or many buildings, such as a city center, registration performance is significantly more accurate when using the PVM. Numéro de notice : A2014-630 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.09.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75046 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario, Canada / Junjie Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 98 (December 2014) . - pp 44 - 57 Titre : A hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario, Canada Type de document : Article/Communication Auteurs : Junjie Zhang, Auteur ; Gunho Sohn, Auteur ; Mathieu Brédif , Auteur Année de publication : 2014 Article en page(s) : pp 44 - 57 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] détection d'objet [Termes IGN] hauteur des arbres [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle stochastique [Termes IGN] Pinophyta [Termes IGN] traitement d'image Résumé : (Auteur) This study presents a hybrid framework for single tree detection from airborne laser scanning (ALS) data by integrating low-level image processing techniques into a high-level probabilistic framework. The proposed approach modeled tree crowns in a forest plot as a configuration of circular objects. We took advantage of low-level image processing techniques to generate candidate configurations from the canopy height model (CHM): the treetop positions were sampled within the over-extracted local maxima via local maxima filtering, and the crown sizes were derived from marker-controlled watershed segmentation using corresponding treetops as markers. The configuration containing the best possible set of detected tree objects was estimated by a global optimization solver. To achieve this, we introduced a Gibbs energy, which contains a data term that judges the fitness of the objects with respect to the data, and a prior term that prevents severe overlapping between tree crowns on the configuration space. The energy was then embedded into a Markov Chain Monte Carlo (MCMC) dynamics coupled with a simulated annealing to find its global minimum. In this research, we also proposed a Monte Carlo-based sampling method for parameter estimation. We tested the method on a temperate mature coniferous forest in Ontario, Canada and also on simulated coniferous forest plots with different degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering, thus increasing the overall detection accuracy by approximately 10% on all of the datasets. Numéro de notice : A2014-631 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.08.007 Date de publication en ligne : 20/10/2014 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.08.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75047 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Domain adaptation for land use classification: A spatio-temporal knowledge reusing method / Yilun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 98 (December 2014) . - pp 133 - 144 Titre : Domain adaptation for land use classification: A spatio-temporal knowledge reusing method Type de document : Article/Communication Auteurs : Yilun Liu, Auteur ; Xia Li, Auteur Année de publication : 2014 Article en page(s) : pp 133 - 144 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classificateur [Termes IGN] classification [Termes IGN] classification dirigée [Termes IGN] connaissance thématique [Termes IGN] données anciennes [Termes IGN] utilisation du sol Résumé : (Auteur) Land use classification requires a significant amount of labeled data, which may be difficult and time consuming to obtain. On the other hand, without a sufficient number of training samples, conventional classifiers are unable to produce satisfactory classification results. This paper aims to overcome this issue by proposing a new model, TrCbrBoost, which uses old domain data to successfully train a classifier for mapping the land use types of target domain when new labeled data are unavailable. TrCbrBoost adopts a fuzzy CBR (Case Based Reasoning) model to estimate the land use probabilities for the target (new) domain, which are subsequently used to estimate the classifier performance. Source (old) domain samples are used to train the classifiers of a revised TrAdaBoost algorithm in which the weight of each sample is adjusted according to the classifier’s performance. This method is tested using time-series SPOT images for land use classification. Our experimental results indicate that TrCbrBoost is more effective than traditional classification models, provided that sufficient amount of old domain data is available. Under these conditions, the proposed method is 9.19% more accurate. Numéro de notice : A2014-632 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.09.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75048 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2014121	RAB	Revue	Centre de documentation	En réserve L003	Disponible