Descripteur
Termes IGN > mathématiques > statistique mathématique > analyse de données > classification > classification par séparateurs à vaste marge
classification par séparateurs à vaste margeSynonyme(s)classification SVMVoir aussi |
Documents disponibles dans cette catégorie (254)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
Etendre la recherche sur niveau(x) vers le bas
Hourly rainfall forecast model using supervised learning algorithm / Qingzhi Zhao in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)
![]()
[article]
Titre : Hourly rainfall forecast model using supervised learning algorithm Type de document : Article/Communication Auteurs : Qingzhi Zhao, Auteur ; Yang Liu, Auteur ; Wanqiang Yao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 4100509 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] autocorrélation
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données GNSS
[Termes IGN] heure
[Termes IGN] modèle de simulation
[Termes IGN] modèle météorologique
[Termes IGN] précipitation
[Termes IGN] série temporelle
[Termes IGN] station GNSS
[Termes IGN] Taïwan
[Termes IGN] vapeur d'eauRésumé : (auteur) Previous studies on short-term rainfall forecast using precipitable water vapor (PWV) and meteorological parameters mainly focus on rain occurrence, while the rainfall forecast is rarely investigated. Therefore, an hourly rainfall forecast (HRF) model based on a supervised learning algorithm is proposed in this study to predict rainfall with high accuracy and time resolution. Hourly PWV derived from Global Navigation Satellite System (GNSS) and temperature data are used as input parameters of the HRF model, and a support vector machine is introduced to train the proposed model. In addition, this model also considers the time autocorrelation of rainfall in the previous epoch. Hourly PWV data of 21 GNSS stations and collocated meteorological parameters (temperature and rainfall) for five years in Taiwan Province are selected to validate the proposed model. Internal and external validation experiments have been performed under the cases of slight, moderate, and heavy rainfall. Average root-mean-square error (RMSE) and relative RMSE of the proposed HRF model are 1.36/1.39 mm/h and 1.00/0.67, respectively. In addition, the proposed HRF model is compared with the similar works in previous studies. Compared results reveal the satisfactory performance and superiority of the proposed HRF model in terms of time resolution and forecast accuracy. Numéro de notice : A2022-024 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3054582 Date de publication en ligne : 09/02/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3054582 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99253
in IEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 4100509[article]Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods / Mir Reza Ghaffari Razin in Advances in space research, vol 69 n° 7 (April 2022)
![]()
[article]
Titre : Modeling of precipitable water vapor from GPS observations using machine learning and tomography methods Type de document : Article/Communication Auteurs : Mir Reza Ghaffari Razin, Auteur ; Behzad Voosoghi, Auteur Année de publication : 2022 Article en page(s) : pp 2671 - 2681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Inférence floue
[Termes IGN] Iran
[Termes IGN] précipitation
[Termes IGN] radiosondage
[Termes IGN] réseau neuronal artificiel
[Termes IGN] retard hydrostatique
[Termes IGN] retard troposphérique zénithal
[Termes IGN] tomographie par GPS
[Termes IGN] vapeur d'eau
[Termes IGN] voxelRésumé : (auteur) This paper studies the application of two machine learning methods to model precipitable water vapor (PWV) using observations of 23 GPS stations from the local GPS network of north-west of Iran in 2011. In a first step, the zenith tropospheric delay (ZTD) and zenith hydrostatic delay (ZHD) is calculated with the Bernese GNSS software and Saastamoinen model as revised by Davis, respectively. Then, by subtracting the ZHD from the ZTD, the zenith wet delay (ZWD) is obtained at each GPS station, for all times. In a second step, ZWD is modeled by two different machine learning methods, based on the latitude, longitude, DOY, time, relative humidity, temperature and pressure. After training a Support Vector Machine (SVM) and an Artificial Neural Network (ANN), ZWD temporal and spatial variations are estimated. Using the formula by Bevis, the ZWD can be converted to PWV at any time and space, for each machine learning method. The accuracy of the two new models is evaluated using control stations, exterior and radiosonde station, whose observations were not used in the training step. Also, all the results of the SVM and ANN are compared with a voxel-based tomography (VBT) model. In the control and exterior stations, ZWD estimated by the SVM (ZWDSVM) and ANN (ZWDANN) is compared with the ZWD obtained from the GPS (ZWDGPS). Also, in the control and exterior stations, precise point positioning (PPP) is used to evaluate the accuracy of the new models. In the radiosonde station, the PWV of the new models (PWVSVM, PWVANN) is compared with the radiosonde PWV (PWVradiosonde) and voxel-based PWV (PWVVBT). The averaged relative error of the SVM, ANN and VBT models in the control stations is 10.50%, 12.71% and 12.91%, respectively. For SVM, ANN and VBT models, the averaged RMSE at the control stations is 1.87 (mm), 2.22 (mm) and 2.29 (mm), respectively. Analysis of the results of PWV estimated by the SVM, ANN and VBT, as well as the surface precipitation obtained from meteorological stations, indicate the high accuracy of the SVM in comparison with the ANN and VBT model. In the results shown in this paper, the SVM has the best ability to accurately estimate ZWD and PWV, using local GPS network observations. Numéro de notice : A2022-446 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1016/j.asr.2022.01.003 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.asr.2022.01.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100106
in Advances in space research > vol 69 n° 7 (April 2022) . - pp 2671 - 2681[article]Multi-criteria geographic analysis for automated cartographic generalization / Guillaume Touya in Cartographic journal (the), vol 59 n° 1 (February 2022)
![]()
[article]
Titre : Multi-criteria geographic analysis for automated cartographic generalization Type de document : Article/Communication Auteurs : Guillaume Touya , Auteur
Année de publication : 2022 Projets : 1-Pas de projet / Article en page(s) : pp 18 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse multicritère
[Termes IGN] analyse spatiale
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] densité du bâti
[Termes IGN] Electre
[Termes IGN] généralisation cartographique automatisée
[Vedettes matières IGN] GénéralisationMots-clés libres : PROMETHEE Résumé : (auteur) Cartographic generalization is a process similar to text summarization that transforms a map when scale is reduced. Cartographic generalization simplifies the map content while preserving as much as possible the initial characteristics and spatial relations of the map. The automation of this process requires a deep understanding of the context of each map feature, which involves different criteria such as the shape of the feature, the semantic nature of the feature, or the spatial patterns of its neighbouring features. This is why multiple criteria decision techniques can be relevant during the process. This paper proposes two use cases of cartographic generalization sub-tasks where multiple criteria decision techniques improve past techniques: the classification of urban building blocks and the ordering of the buildings to keep in the map as a priority. In both use cases, the proposed methods are experimented on large real cartographic datasets, and evaluated in comparison to alternative techniques. Numéro de notice : A2022-075 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2020.1858608 Date de publication en ligne : 26/07/2021 En ligne : https://doi.org/10.1080/00087041.2020.1858608 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98217
in Cartographic journal (the) > vol 59 n° 1 (February 2022) . - pp 18 - 34[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 030-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Multi-view urban scene classification with a complementary-information learning model / Wanxuan Geng in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)
![]()
[article]
Titre : Multi-view urban scene classification with a complementary-information learning model Type de document : Article/Communication Auteurs : Wanxuan Geng, Auteur ; Weixun Zhou, Auteur ; Shuanggen Jin, Auteur Année de publication : 2022 Article en page(s) : pp 65 - 72 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données de terrain
[Termes IGN] données multisources
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données multisource
[Termes IGN] image aérienne
[Termes IGN] niveau du sol
[Termes IGN] précision de la classification
[Termes IGN] scène urbaineRésumé : (Auteur) Traditional urban scene-classification approaches focus on images taken either by satellite or in aerial view. Although single-view images are able to achieve satisfactory results for scene classification in most situations, the complementary information provided by other image views is needed to further improve performance. Therefore, we present a complementary information-learning model (CILM) to perform multi-view scene classification of aerial and ground-level images. Specifically, the proposed CILM takes aerial and ground-level image pairs as input to learn view-specific features for later fusion to integrate the complementary information. To train CILM, a unified loss consisting of cross entropy and contrastive losses is exploited to force the network to be more robust. Once CILM is trained, the features of each view are extracted via the two proposed feature-extraction scenarios and then fused to train the support vector machine classifier for classification. The experimental results on two publicly available benchmark data sets demonstrate that CILM achieves remarkable performance, indicating that it is an effective model for learning complementary information and thus improving urban scene classification. Numéro de notice : A2022-063 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00062R2 Date de publication en ligne : 01/01/2022 En ligne : https://doi.org/10.14358/PERS.21-00062R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99708
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 1 (January 2022) . - pp 65 - 72[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2022011 SL Revue Centre de documentation Revues en salle Disponible A prediction model for surface deformation caused by underground mining based on spatio-temporal associations / Min Ren in Geomatics, Natural Hazards and Risk, vol 13 (2022)
![]()
[article]
Titre : A prediction model for surface deformation caused by underground mining based on spatio-temporal associations Type de document : Article/Communication Auteurs : Min Ren, Auteur ; Guanwen Cheng, Auteur ; Wancheng Zhu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 94 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse des risques
[Termes IGN] analyse spatio-temporelle
[Termes IGN] Chine
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déformation de la croute terrestre
[Termes IGN] déformation de surface
[Termes IGN] mine de fer
[Termes IGN] modèle de simulation
[Termes IGN] règle d'associationMots-clés libres : spatio-temporal association rule mining (STARM) Résumé : (auteur) Accurate predictions of the surface deformation caused by underground mining are crucial for the safe development of underground resources. Although surface deformation has been predicted by artificial intelligence (AI) methods, most AI models are established based on the relationships between surface deformation and influential factors. The lack of consideration of the deformation state transition often leads to errors in the prediction results of catastrophic deformation by conventional AI methods. In this respect, this study introduces a surface deformation prediction model based on spatio-temporal association rule mining (STARM). Surface deformation is classified as excessive deformation zone (EDZ) and hysteretic deformation zone (HDZ), representing different surface deformation stage or state. The spatio-temporal association rules between the monitored EDZ and HDZ data are then mined. A surface deformation prediction model is established according to the spatio-temporal relationship between monitored EDZ and HDZ data. The proposed model is verified based on a practical case study of the Chengchao Iron Mine in China. The data collection of the influential factors is not requisite for the proposed model. It can achieve accurate prediction of the catastrophic deformation that was characterized by deformation state transition. Numéro de notice : A2022-035 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/19475705.2021.2015460 Date de publication en ligne : 21/12/2021 En ligne : https://doi.org/10.1080/19475705.2021.2015460 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99359
in Geomatics, Natural Hazards and Risk > vol 13 (2022) . - pp 94 - 122[article] PermalinkTowards urban flood susceptibility mapping using data-driven models in Berlin, Germany / Omar Seleem in Geomatics, Natural Hazards and Risk, vol 13 (2022)
PermalinkA comparative approach of support vector machine kernel functions for GIS-based landslide susceptibility mapping / Khalil Valizadeh Kamran in Applied geomatics, vol 13 n° 4 (December 2021)
PermalinkFlexible Gabor-based superpixel-level unsupervised LDA for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
PermalinkDisaster intensity-based selection of training samples for remote sensing building damage classification / Luis Moya in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)
PermalinkClassification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery / Simbarashe Jombo in Applied geomatics, vol 13 n° 3 (September 2021)
PermalinkMulti-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)
PermalinkImproving urban land cover classification with combined use of Sentinel-2 and Sentinel-1 imagery / Bin Hu in ISPRS International journal of geo-information, vol 10 n° 8 (August 2021)
PermalinkInvestigating the application of artificial intelligence for earthquake prediction in Terengganu / Suzlyana Marhain in Natural Hazards, vol 108 n° 1 (August 2021)
PermalinkSurface modelling of forest aboveground biomass based on remote sensing and forest inventory data / Xiaofang Sun in Geocarto international, vol 36 n° 14 ([01/08/2021])
Permalink