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Region level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)
[article]
Titre : Region level SAR image classification using deep features and spatial constraints Type de document : Article/Communication Auteurs : Anjun Zhang, Auteur ; Xuezhi Yang, Auteur ; Shuai Fang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 36-48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] carte de confiance
[Termes IGN] champ aléatoire de Markov
[Termes IGN] chatoiement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image radar moirée
[Termes IGN] lissage de données
[Termes IGN] modélisation spatiale
[Termes IGN] précision de la classification
[Termes IGN] superpixelRésumé : (auteur) The region-level SAR image classification algorithms which combine CNN (Convolutional Neural Networks) with super-pixel have been proposed to enhance the classification accuracy compared with the pixel-level algorithms. However, the spatial constraints between the super-pixel regions are not considered, which may limit the performance of these algorithms. To address this problem, an RCC-MRF (RCC, Region Category Confidence-degree) and CNN based region-level SAR image classification algorithm which explores the deep features extracted by CNN and the spatial constraints between super-pixel regions is proposed in this paper. The initial labels of super-pixel regions are obtained using a voting strategy based on the predicted labels CNN. The unary energy function of RCC-MRF is designed to find the category that a region most probably belongs to by using the RCC term which is constructed based on the probability distributions over all categories of pixels predicted by CNN. The binary energy function of RCC-MRF explores the spatial constraints between the adjacent super-pixel regions. In our proposed algorithm, the pixel-level misclassifications can be reduced by the smoothing within regions and the region-level misclassifications will be rectified by minimizing the energy function of RCC-MRF. Experiments have been done on simulated and real SAR images to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm notably outperforms the other CNN-based region-level SAR image classification algorithms. Numéro de notice : A2020-136 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.001 Date de publication en ligne : 07/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94752
in ISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 36-48[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors / Boris Kargoll in Journal of geodesy, vol 94 n° 5 (May 2020)
[article]
Titre : Self-tuning robust adjustment within multivariate regression time series models with vector-autoregressive random errors Type de document : Article/Communication Auteurs : Boris Kargoll, Auteur ; Gaël Kermarrec, Auteur ; Hamza Alkhatib, Auteur ; Johannes Korte, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] algorithme espérance-maximisation
[Termes IGN] analyse vectorielle
[Termes IGN] auto-régression
[Termes IGN] bruit blanc
[Termes IGN] corrélation croisée normalisée
[Termes IGN] erreur aléatoire
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle stochastique
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] station GPS
[Termes IGN] valeur aberranteRésumé : (auteur) The iteratively reweighted least-squares approach to self-tuning robust adjustment of parameters in linear regression models with autoregressive (AR) and t-distributed random errors, previously established in Kargoll et al. (in J Geod 92(3):271–297, 2018. https://doi.org/10.1007/s00190-017-1062-6), is extended to multivariate approaches. Multivariate models are used to describe the behavior of multiple observables measured contemporaneously. The proposed approaches allow for the modeling of both auto- and cross-correlations through a vector-autoregressive (VAR) process, where the components of the white-noise input vector are modeled at every time instance either as stochastically independent t-distributed (herein called “stochastic model A”) or as multivariate t-distributed random variables (herein called “stochastic model B”). Both stochastic models are complementary in the sense that the former allows for group-specific degrees of freedom (df) of the t-distributions (thus, sensor-component-specific tail or outlier characteristics) but not for correlations within each white-noise vector, whereas the latter allows for such correlations but not for different dfs. Within the observation equations, nonlinear (differentiable) regression models are generally allowed for. Two different generalized expectation maximization (GEM) algorithms are derived to estimate the regression model parameters jointly with the VAR coefficients, the variance components (in case of stochastic model A) or the cofactor matrix (for stochastic model B), and the df(s). To enable the validation of the fitted VAR model and the selection of the best model order, the multivariate portmanteau test and Akaike’s information criterion are applied. The performance of the algorithms and of the white noise test is evaluated by means of Monte Carlo simulations. Furthermore, the suitability of one of the proposed models and the corresponding GEM algorithm is investigated within a case study involving the multivariate modeling and adjustment of time-series data at four GPS stations in the EUREF Permanent Network (EPN). Numéro de notice : A2020-291 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01376-6 Date de publication en ligne : 10/05/2020 En ligne : https://doi.org/10.1007/s00190-020-01376-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95120
in Journal of geodesy > vol 94 n° 5 (May 2020)[article]Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia / Abdulkader Murad in ISPRS International journal of geo-information, vol 9 n° 5 (May 2020)
[article]
Titre : Using GIS for disease mapping and clustering in Jeddah, Saudi Arabia Type de document : Article/Communication Auteurs : Abdulkader Murad, Auteur ; Bandar Fuad Khashoggi, Auteur Année de publication : 2020 Article en page(s) : 22 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] analyse de groupement
[Termes IGN] Arabie Saoudite
[Termes IGN] carte sanitaire
[Termes IGN] distribution spatiale
[Termes IGN] estimation par noyau
[Termes IGN] modélisation environnementale
[Termes IGN] modélisation spatiale
[Termes IGN] surveillance sanitaire
[Termes IGN] zone à risqueRésumé : (auteur) Geographic information systems (GIS) can be used to map the geographical distribution of the prevalence of disease, trends in disease transmission, and to spatially model environmental aspects of disease occurrence. The aim of this study is to discuss a GIS application created to produce mapping and cluster modeling of three diseases in Jeddah, Saudi Arabia: diabetes, asthma, and hypertension. Data about these diseases were obtained from health centers’ registered patient records. These data were spatially evaluated using several spatial–statistical analytical models, including kernel and hotspot models. These models were created to explore and display the disparate patterns of the selected diseases and to illustrate areas of high concentration, and may be invaluable in understanding local patterns of diseases and their geographical associations. Numéro de notice : A2020-300 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9050328 Date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.3390/ijgi9050328 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95140
in ISPRS International journal of geo-information > vol 9 n° 5 (May 2020) . - 22 p.[article]A single-receiver geometry-free approach to stochastic modeling of multi-frequency GNSS observables / Baocheng Zhang in Journal of geodesy, vol 94 n°4 (April 2020)
[article]
Titre : A single-receiver geometry-free approach to stochastic modeling of multi-frequency GNSS observables Type de document : Article/Communication Auteurs : Baocheng Zhang, Auteur ; Pengyu Hou, Auteur ; Teng Liu, Auteur ; Yunbin Yuan, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] analyse de variance
[Termes IGN] corrélation croisée normalisée
[Termes IGN] corrélation temporelle
[Termes IGN] fréquence multiple
[Termes IGN] méthode des moindres carrés
[Termes IGN] méthode robuste
[Termes IGN] modèle stochastique
[Termes IGN] positionnement ponctuel précis
[Termes IGN] récepteur
[Termes IGN] traitement de données GNSS
[Termes IGN] trajet multipleRésumé : (auteur) The proper choice of stochastic model is of great importance to global navigation satellite system (GNSS) data processing. Whereas extensive investigations into stochastic modeling are mainly based on the relative (or differential) method employing zero and/or short baselines, this work proposes an absolute method that relies upon a stand-alone receiver and works by applying the least-squares variance component estimation to the geometry-free functional model, thus facilitating the characterization of stochastic properties of multi-frequency GNSS observables at the undifferenced level. In developing the absolute method, special care has been taken of the code multipath effects by introducing ambiguity-like parameters to the code observation equations. By means of both the relative and absolute methods, we characterize the precision, cross and time correlation of the code and phase observables of two newly emerging constellations, namely the Chinese BDS and the European Galileo, collected by a variety of receivers of different types at multiple frequencies. Our first finding is that so far as the precision is concerned, the absolute method yields nearly the same numerical values as those derived by the zero-baseline-based relative method. However, the two methods give contradictory results with regard to the cross correlation, which is found (not) to occur between BDS phase observables when use has been made of the relative (absolute) method. Our explanation to this discrepancy is that the cross correlation found in the relative method originates from the parts (antenna, cable, low noise amplifier) shared by two receivers creating a zero baseline. The time correlation is only of significance when the multipath effects are present, as is the case with the short-baseline-based relative method; this correlation turns out to be largely weaker (or ideally absent) in the absolute (or zero-baseline-based relative) method. Moreover, with the absolute method, the stochastic properties determined for two receivers of the same type but subject to different multipath effects are virtually the same. We take this as a convincing evidence that the absolute method is robust against multipath effects. Hence, the absolute method proposed in the present work represents a promising complement to the relative method and appears to be particularly beneficial to GNSS positioning, navigation and timing technologies based on the undifferenced observables, typically the precise point positioning. Numéro de notice : A2020-160 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01366-8 Date de publication en ligne : 09/03/2020 En ligne : https://doi.org/10.1007/s00190-020-01366-8 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94817
in Journal of geodesy > vol 94 n°4 (April 2020)[article]Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India / Biswajit Mondal in Geocarto international, vol 35 n° 4 ([15/03/2020])
[article]
Titre : Comparison of spatial modelling approaches to simulate urban growth: a case study on Udaipur city, India Type de document : Article/Communication Auteurs : Biswajit Mondal, Auteur ; Suman Chakraborti, Auteur ; Dipendra Nath Das, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 411 - 433 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] analyse multicritère
[Termes IGN] automate cellulaire
[Termes IGN] chaîne de Markov
[Termes IGN] croissance urbaine
[Termes IGN] étalement urbain
[Termes IGN] Inde
[Termes IGN] modèle de simulation
[Termes IGN] modélisation spatiale
[Termes IGN] pente
[Termes IGN] Perceptron multicouche
[Termes IGN] utilisation du solRésumé : (auteur) Assessment of past and future urban growth processes helps the decision makers to evaluate and formulate the policy documents. In an attempt to make such assessments, this study compares three commonly used urban growth models: Multicriteria Cellular Automata-Markov Chain (MCCA-MC), Multi-Layer Perception Markov Chain (MLP-MC), and the Slope, Land use, Exclusion, Urban Extent, Transportation and Hillshade (SLEUTH). This study has taken into account the land use and land cover data for the years, 1977, 1992, 2000, 2008, 2016 and prepared driving variables for urban growth. The KAPPA index of agreement indicates that the MCCA-MC, MLP-MC and SLEUTH models avoid errors by 94%, 93%, and 92% respectively. Models forecast that about 156.96 km2, 157.43 km2 and 142.43 km2 built-up areas will emerge through the process of urbanization by 2031 in the city of Udaipur. However, this assessment identified that all the models are embodied with their own advantages and disadvantages while serving specific purposes. While the MCCA-MC and MLP-MC provides a good account of the urban spread, the SLEUTH identifies the new isolated growth centres more accurately. Numéro de notice : A2020-100 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520922 Date de publication en ligne : 03/01/2019 En ligne : https://doi.org/10.1080/10106049.2018.1520922 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94691
in Geocarto international > vol 35 n° 4 [15/03/2020] . - pp 411 - 433[article]Bayesian inversion of convolved hidden Markov models with applications in reservoir prediction / Torstein Fjeldstad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkA sequential Monte Carlo framework for noise filtering in InSAR time series / Mehdi Khaki in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkLand use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process / Biswajit Nath in ISPRS International journal of geo-information, vol 9 n° 2 (February 2020)PermalinkA spatio-temporal deformation model for laser scanning point clouds / Corinna Harmening in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkInteractive display of surnames distributions in historic and contemporary Great Britain / Justin Van Dijk in Journal of maps, vol 16 n° 1 ([02/01/2020])PermalinkPermalinkPermalinkAsymptotically exact data augmentation : models and Monte Carlo sampling with applications to Bayesian inference / Maxime Vono (2020)PermalinkPermalinkCamera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs / Grégoire Guillet in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)Permalink