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Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3778 - 3790 Titre : Ensemble learning for hyperspectral image classification using tangent collaborative representation Type de document : Article/Communication Auteurs : Hongjun Su, Auteur ; Yao Yu, Auteur ; Qian Du, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3778 - 3790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] conception collaborative [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] neurone artificiel [Termes descripteurs IGN] performance [Termes descripteurs IGN] régression Résumé : (auteur) Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging. Numéro de notice : 25280 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2957135 date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2957135 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95100 [article]

Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation / Shuhui Gong in International journal of geographical information science IJGIS, vol 34 n° 6 (June 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 6 (June 2020) . - pp 1210 - 1234 Titre : Extracting activity patterns from taxi trajectory data: a two-layer framework using spatio-temporal clustering, Bayesian probability and Monte Carlo simulation Type de document : Article/Communication Auteurs : Shuhui Gong, Auteur ; John Cartlidge, Auteur ; Ruibin Bai, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1210 - 1234 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] comportement [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] longueur de trajet [Termes descripteurs IGN] méthode de Monte-Carlo [Termes descripteurs IGN] origine - destination [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] population urbaine [Termes descripteurs IGN] questionnaire [Termes descripteurs IGN] taxi [Termes descripteurs IGN] temps de trajet [Termes descripteurs IGN] voyage Résumé : (auteur) Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips. Numéro de notice : A2020-270 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1641715 date de publication en ligne : 19/07/2019 En ligne : https://doi.org/10.1080/13658816.2019.1641715 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95042 [article]

Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) / Chanwoo Jin in Transactions in GIS, Vol 24 n° 1 (February 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 1 (February 2020) . - pp 104 - 122 Titre : Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) Type de document : Article/Communication Auteurs : Chanwoo Jin, Auteur ; Atsushi Nara, Auteur ; Jiue-An Yang, Auteur ; Ming-Hsiang Tsou, Auteur Année de publication : 2020 Article en page(s) : pp 104 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] carte de mobilité [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] interpolation par pondération de zones [Termes descripteurs IGN] mesure de similitude [Termes descripteurs IGN] mobilité [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] origine - destination Résumé : (auteur) Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin–destination (OD) matrices by spatially extending an image‐assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue due to the ordering of OD pairs by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census‐based Longitudinal Employer–Household Dynamics Origin–Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media. Numéro de notice : A2020-104 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12590 date de publication en ligne : 23/10/2019 En ligne : https://doi.org/10.1111/tgis.12590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94698 [article]

Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators / Dinesh Babu Irulappa Pillai Vijayakumar in Remote sensing, vol 11 n° 8 (August 2019)  

Public [article]  inRemote sensing > vol 11 n° 8 (August 2019) Titre : Increasing precision for French forest inventory estimates using the k-NN technique with optical and photogrammetric data and model-assisted estimators Type de document : Article/Communication Auteurs : Dinesh Babu Irulappa Pillai Vijayakumar, Auteur ; Jean-Pierre Renaud, Auteur ; François Morneau, Auteur ; Ronald E. McRoberts, Auteur ; Cédric Vega , Auteur Année de publication : 2019 Projets : DIABOLO / Packalen, Tuula Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] arbre caducifolié [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] feuillu [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier national (données France) [Termes descripteurs IGN] modèle numérique de surface de la canopée [Termes descripteurs IGN] Orléans, forêt d' (Loiret) [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] Pinus pinaster [Termes descripteurs IGN] Pinus sylvestris [Termes descripteurs IGN] quercus pedunculata [Termes descripteurs IGN] quercus sessiliflora [Termes descripteurs IGN] Sologne (France) [Vedettes matières IGN] Inventaire forestier Résumé : (auteur) Multisource forest inventory methods were developed to improve the precision of national forest inventory estimates. These methods rely on the combination of inventory data and auxiliary information correlated with forest attributes of interest. As these methods have been predominantly tested over coniferous forests, the present study used this approach for heterogeneous and complex deciduous forests in the center of France. The auxiliary data considered included a forest type map, Landsat 8 spectral bands and derived vegetation indexes, and 3D variables derived from photogrammetric canopy height models. On a subset area, changes in canopy height estimated from two successive photogrammetric models were also used. A model-assisted inference framework, using a k nearest-neighbors approach, was used to predict 11 field inventory variables simultaneously. The results showed that among the auxiliary variables tested, 3D metrics improved the precision of dendrometric estimates more than other auxiliary variables. Relative efficiencies (RE) varying from 2.15 for volume to 1.04 for stand density were obtained using all auxiliary variables. Canopy height changes also increased RE from 3% to 26%. Our results confirmed the importance of 3D metrics as auxiliary variables and demonstrated the value of canopy change variables for increasing the precision of estimates of forest structural attributes such as density and quadratic mean diameter. Numéro de notice : A2019-382 Thématique : FORET/IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs11080991 date de publication en ligne : 25/04/2019 En ligne : https://doi.org/10.3390/rs11080991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93456 [article]

On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact / Amir Khodabandeh in GPS solutions, vol 23 n° 3 (July 2019)  

Public [article]  inGPS solutions > vol 23 n° 3 (July 2019) Titre : On the detectability of mis-modeled biases in the network-derived positioning corrections and their user impact Type de document : Article/Communication Auteurs : Amir Khodabandeh, Auteur ; Jinling Wang, Auteur ; Chris Rizos, Auteur ; Ahmed El-Mowafy, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] correction [Termes descripteurs IGN] filtre de Kalman [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] intégrité des données [Termes descripteurs IGN] modèle linéaire [Termes descripteurs IGN] positionnement cinématique en temps réel [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] positionnement ponctuel précis [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] Receiver Autonomous Integrity Monitoring [Termes descripteurs IGN] traitement de données GNSS Résumé : (auteur) High-precision single-receiver positioning requires the provision of reliable network-derived corrections. Care must therefore be exercised to continuously check the quality of the corrections and to detect the possible presence of mis-modeled biases in the network data. In network-RTK or its state-space implementation, PPP-RTK, quality control of the solutions is executed in two separate phases: the network component and the user component. Once confidence in the network-derived solutions is declared, a subset of the solutions is sent as corrections to a single-receiver user, thereby allowing the user to separately check the integrity of his network-aided model. In such a two-step integrity monitoring procedure, an intermediate step is missing, the integrity monitoring of the corrections themselves. It is the goal of this contribution to provide a quality control procedure for GNSS parameter solutions at the correction level, and to measure the impact a missed detection bias has on the (ambiguity-resolved) user position. New detection test statistics are derived with which the single-receiver user can check the overall validity of the corrections even before applying them to his data. A small-scale network of receivers is utilized to provide numerical insights into the detectability of mis-modeled biases using the proposed detectors and to analyze the impact of such biases on the user positioning performance. Numéro de notice : A2019-195 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-019-0863-x date de publication en ligne : 14/05/2019 En ligne : https://doi.org/10.1007/s10291-019-0863-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92649 [article]

A breakpoint detection in the mean model with heterogeneous variance on fixed time-intervals / Olivier Bock in Statistics and Computing, vol 29 Online First (May 2019)  

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Excel data analysis / Hector Guerrero (2019)  

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Uncertainty in radar emitter classification and clustering / Guillaume Revillon (2019)  

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How to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes / Niko Kulha in Forests, vol 9 n° 10 (October 2018)  

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A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)  

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Parametric bootstrap estimators for hybrid inference in forest inventories / Mathieu Fortin in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)  

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Inference on forest attributes and ecological diversity of trees outside forest by a two-phase inventory / Marco Marchetti in Annals of Forest Science [en ligne], vol 75 n° 2 (June 2018)  

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Un inventaire forestier multisource pour la gestion des territoires / Dinesh Babu Irulappa Pillai Vijayakumar (2018) 

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Recherche en matière d’inventaire forestier [Introduction] / Jean-Daniel Bontemps (2018) 

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Model-dependent forest stand-level inference with and without estimates of stand-effects / Magnussen, Steen in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)  

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