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Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning / Junwei Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
[article]
Titre : Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning Type de document : Article/Communication Auteurs : Junwei Han, Auteur ; Dingwen Zhang, Auteur ; Gong Cheng, Auteur Année de publication : 2015 Article en page(s) : pp 3325 - 3337 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] détection d'objet
[Termes IGN] estimation bayesienne
[Termes IGN] état de l'art
[Termes IGN] moteur d'inférenceRésumé : (Auteur) The abundant spatial and contextual information provided by the advanced remote sensing technology has facilitated subsequent automatic interpretation of the optical remote sensing images (RSIs). In this paper, a novel and effective geospatial object detection framework is proposed by combining the weakly supervised learning (WSL) and high-level feature learning. First, deep Boltzmann machine is adopted to infer the spatial and structural information encoded in the low-level and middle-level features to effectively describe objects in optical RSIs. Then, a novel WSL approach is presented to object detection where the training sets require only binary labels indicating whether an image contains the target object or not. Based on the learnt high-level features, it jointly integrates saliency, intraclass compactness, and interclass separability in a Bayesian framework to initialize a set of training examples from weakly labeled images and start iterative learning of the object detector. A novel evaluation criterion is also developed to detect model drift and cease the iterative learning. Comprehensive experiments on three optical RSI data sets have demonstrated the efficacy of the proposed approach in benchmarking with several state-of-the-art supervised-learning-based object detection approaches. Numéro de notice : A2015 - 283 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2374218 Date de publication en ligne : 18/12/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2374218 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76400
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3325 - 3337[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible Outlier Detection by means of Monte Carlo Estimation including resistant Scale Estimation / Christian Marx in Journal of applied geodesy, vol 9 n° 2 (June 2015)
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Titre : Outlier Detection by means of Monte Carlo Estimation including resistant Scale Estimation Type de document : Article/Communication Auteurs : Christian Marx, Auteur Année de publication : 2015 Article en page(s) : pp 123 - 142 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] compensation par moindres carrés
[Termes IGN] détection d'anomalie
[Termes IGN] estimation statistique
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode robuste
[Termes IGN] valeur aberranteRésumé : (auteur) The identification of outliers in measurement data is hindered if they are present in leverage points as well as in rest of the data. A promising method for their identification is the Monte Carlo estimation (MCE), which is subject of the present investigation. In MCE the data are searched for data subsamples without leverage outliers and with few (or no) non-leverage outliers by a random generation of subsamples. The required number of subsamples by which several of such subsamples are generated with a given probability is derived. Each generated subsample is rated based on the residuals resulting from an adjustment. By means of a simulation it is shown that a least squares adjustment is suitable. For the rating of the subsamples, the sum of squared residuals is used as a measure of the fit. It is argued that this (unweighted) sum is also appropriate if data have unequal weights. An investigation of the robustness of a final Bayes estimation with the result of the Monte Carlo search as prior information reveals its inappropriateness. Furthermore, the case of an unknown variance factor is considered. A simulation for different scale estimators for the variance factor shows their impracticalness. A new resistant scale estimator is introduced which is based on a generalisation of the median absolut deviation. Taking into account the results of the investigations, a new procedure for MCE considering a scale estimation is proposed. Finally, this method is tested by simulation. MCE turns out to be more reliable in the identification of outliers than a conventional resistant estimation method. Numéro de notice : A2015-392 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2014-0029 En ligne : http://www.degruyter.com/view/j/jag.2014.9.issue-2/jag-2014-0029/jag-2014-0029.x [...] Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76863
in Journal of applied geodesy > vol 9 n° 2 (June 2015) . - pp 123 - 142[article]Real-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting / Junbo Shi in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
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Titre : Real-time GPS precise point positioning-based precipitable water vapor estimation for rainfall monitoring and forecasting Type de document : Article/Communication Auteurs : Junbo Shi, Auteur ; Chaoqian Xu, Auteur ; Jiming Guo, Auteur ; Yang Gao, Auteur Année de publication : 2015 Article en page(s) : pp 3452 - 3459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] estimation statistique
[Termes IGN] positionnement cinématique en temps réel
[Termes IGN] positionnement par GPS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] surveillance météorologique
[Termes IGN] vapeur d'eauRésumé : (Auteur) GPS-based precipitable water vapor (PWV) estimation has been proven as a cost-effective approach for numerical weather prediction. Most previous efforts focus on the performance evaluation of post-processed GPS-derived PWV estimates using International GNSS Service (IGS) satellite products with at least 3-9-h latency. However, the suggested timeliness for meteorological nowcasting is 5-30 min. Therefore, the latency has limited the GPS-based PWV estimation in real-time meteorological nowcasting. The limitation has been overcome since April 2013 when IGS released real-time GPS orbit and clock products. This becomes the focus of this paper, which investigates real-time GPS precise point positioning (PPP)-based PWV estimation and its potential for rainfall monitoring and forecasting. This paper first evaluates the accuracy of IGS CLK90 real-time orbit and clock products. Root-mean-square (RMS) errors of Numéro de notice : A2015-279 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2377041 Date de publication en ligne : 22/12/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2377041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76390
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3452 - 3459[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible Minimal detectable outliers as measures of reliability / Karl Rudolf Koch in Journal of geodesy, vol 89 n° 5 (May 2015)
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Titre : Minimal detectable outliers as measures of reliability Type de document : Article/Communication Auteurs : Karl Rudolf Koch, Auteur Année de publication : 2015 Article en page(s) : pp 483-490 Note générale : Bibliographe Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie
[Termes IGN] B-Spline
[Termes IGN] erreur systématique
[Termes IGN] fiabilité des données
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] réseau géodésique
[Termes IGN] valeur aberranteRésumé : (auteur) The concept of reliability was introduced into geodesy by Baarda (A testing procedure for use in geodetic networks. Publications on Geodesy, vol. 2. Netherlands Geodetic Commission, Delft, 1968). It gives a measure for the ability of a parameter estimation to detect outliers and leads in case of one outlier to the MDB, the minimal detectable bias or outlier. The MDB depends on the non-centrality parameter of the χ2-distribution, as the variance factor of the linear model is assumed to be known, on the size of the outlier test of an individual observation which is set to 0.001 and on the power of the test which is generally chosen to be 0.80. Starting from an estimated variance factor, the F-distribution is applied here. Furthermore, the size of the test of the individual observation is a function of the number of outliers to keep the size of the test of all observations constant, say 0.05. The power of the test is set to 0.80. The MDBs for multiple outliers are derived here under these assumptions. The method is applied to the reconstruction of a bell-shaped surface measured by a laser scanner. The MDBs are introduced as outliers for the alternative hypotheses of the outlier tests. A Monte Carlo method reveals that due to the way of introducing the outliers, the false null hypotheses cannot be rejected on the average with a power of 0.80 if the MDBs are not enlarged by a factor. Numéro de notice : A2015-348 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-015-0793-5 Date de publication en ligne : 12/02/2015 En ligne : https://doi.org/10.1007/s00190-015-0793-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76722
in Journal of geodesy > vol 89 n° 5 (May 2015) . - pp 483-490[article]A probabilistic eco-hydrological model to predict the effects of climate change on natural vegetation at a regional scale / Jan-Philip M. Witte in Landscape ecology, vol 30 n° 5 (May 2015)
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Titre : A probabilistic eco-hydrological model to predict the effects of climate change on natural vegetation at a regional scale Type de document : Article/Communication Auteurs : Jan-Philip M. Witte, Auteur ; Ruud P. Bartholomeus, Auteur ; Peter M. van Bodegom, Auteur ; D. Gijsbert Cirkel, Auteur ; Remco van Ek, Auteur ; Yuki Fujita, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 835 - 854 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] changement climatique
[Termes IGN] facteur édaphique
[Termes IGN] habitat (nature)
[Termes IGN] hydrologie
[Termes IGN] impact sur l'environnement
[Termes IGN] modèle de simulation
[Termes IGN] modèle stochastique
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Climate change may hamper the preservation of nature targets, but may create new potential hotspots of biodiversity as well. To timely design adequate measures, information is needed about the feasibility of nature targets under a future climate. Habitat distribution models may provide this, but current models have certain drawbacks: they apply indirect empirical relationships between habitat and vegetation, they often disregard spatially explicit information about groundwater, and they are designed for too coarse spatial scales. We introduce a model that explicitly takes into account spatial effects through groundwater and that can easily be adapted to new scientific approaches and the needs of end-users. It combines (spatially explicit) data sources, transfer functions derived from mechanistic models, and robust relationships between habitat factors and plant characteristics. Outputs are maps showing the occurrence probabilities of vegetation types and their associated conservation values, both on a spatial scale that fits the needs of nature managers and spatial planners. The model was applied to a catchment of 270 km2 to forecast, on a 25 m resolution, the effects of a national climate scenario (related to IPCC A2 and A1B). Computation time was a couple of minutes on a standard PC. Severe loss was predicted for wet and mesotrophic species-rich grasslands, while vegetation of dry and acidic soils appeared to profit. The results were not univocal though, and could probably not have been foreseen on the basis of expert judgement and logic alone, especially because of edaphic factors and spatial hydrological relationships. Numéro de notice : A2015--033 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.1007/s10980-014-0086-z Date de publication en ligne : 29/08/2014 En ligne : http://dx.doi.org/10.1007/s10980-014-0086-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81112
in Landscape ecology > vol 30 n° 5 (May 2015) . - pp 835 - 854[article]Spectral–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)PermalinkThe guided bilateral filter: When the joint/cross bilateral filter becomes robust / Laurent Caraffa in IEEE Transactions on image processing, vol 24 n° 4 (April 2015)PermalinkForest inventory attribute estimation using airborne laser scanning, aerial stereo imagery, radargrammetry and interferometry–Finnish experiences of the 3D techniques / Markus Holopainen in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)PermalinkRoad marking extraction using a model&data-driven RJ-MCMC / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)PermalinkChamp de vitesse GPS du Nord-Est de la France : apport des stations permanentes pour une précision submillimétrique / Eric Henrion in XYZ, n° 142 (mars - mai 2015)PermalinkEvaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa / Timothy Dube in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)PermalinkReview and principles of PPP-RTK methods / Peter J.G. Teunissen in Journal of geodesy, vol 89 n° 3 (March 2015)PermalinkSequential estimation of surface water mass changes from daily satellite gravimetry data / Guillaume L. Ramilien in Journal of geodesy, vol 89 n° 3 (March 2015)PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkJoint segmentation of multiple GPS coordinate series / Julien Gazeaux in Journal de la Société Française de Statistique, vol 156 n° 4 ([01/02/2015])Permalink