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Pansharpening: context-based generalized Laplacian pyramids by robust regression / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6152 - 6167 Titre : Pansharpening: context-based generalized Laplacian pyramids by robust regression Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Stefano Marano, Auteur ; Jocelyn Chanussot, Auteur Année de publication : 2020 Article en page(s) : pp 6152 - 6167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] fonction de transfert de modulation [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] régression [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) Pansharpening refers to the combination of panchromatic (PAN) and multispectral (MS) images, designed to obtain a fused product retaining the fine spatial resolution of the former and the high spectral content of the latter. One of the most popular and successful approaches to pansharpening is the method known as context-based generalized Laplacian pyramid, which requires as a key ingredient for the estimation of the so-called injection coefficients. In this article, we propose the adoption of robust techniques for the estimation of the injection coefficients and detection strategies to select the clusters for which robust regression is needed, providing a suitable balancing between fusion performance and computational burden. Experimental results conducted on five real data sets acquired by the sensors QuickBird, WorldView-3, and WorldView-4, show the superiority of the proposed method with respect to current state-of-the-art pansharpening techniques. Numéro de notice : A2020-528 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2974806 date de publication en ligne : 04/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2974806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95706 [article]

Water level prediction from social media images with a multi-task ranking approach / P. Chaudhary in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 252 - 262 Titre : Water level prediction from social media images with a multi-task ranking approach Type de document : Article/Communication Auteurs : P. Chaudhary, Auteur ; Stefano D'Aronco, Auteur ; João P. Leitão, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 252 - 262 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] inondation [Termes descripteurs IGN] niveau hydrostatique [Termes descripteurs IGN] régression [Termes descripteurs IGN] réseau social [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] vision par ordinateur Résumé : (auteur) Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DeepFlood, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with 11 cm root mean square error. Numéro de notice : A2020-549 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.003 date de publication en ligne : 29/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95776 [article]

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A local projection for integrating geodetic and terrestrial coordinate systems / Mike Bremmer in Survey review, vol 52 n° 374 (August 2020)  

Public [article]  inSurvey review > vol 52 n° 374 (August 2020) . - pp 394 - 402 Titre : A local projection for integrating geodetic and terrestrial coordinate systems Type de document : Article/Communication Auteurs : Mike Bremmer, Auteur ; Marcelo Santos, Auteur Année de publication : 2020 Article en page(s) : pp 394 - 402 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Projections [Termes descripteurs IGN] coordonnées cartésiennes géocentriques [Termes descripteurs IGN] coordonnées géodésiques [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] géodésie terrestre [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] projection [Termes descripteurs IGN] projection stéréographique [Termes descripteurs IGN] système de coordonnées Résumé : (auteur) This paper develops a system for projecting coordinates from a geocentric coordinate system to a topocentric coordinate system defined by terrestrial measurements. The proposed system uses an extension of the Stereographic Double Map Projection and a parametric least squares estimation to calculate the parameters for the map projection. The Extended Stereographic Double Projection and the projection parameter estimation are implemented and tested. Tests are performed to determine rate that discrepancies occur and the maximum extents of the Extended Stereographic Double Projection. This maximum area is determined to be ∼10 km2 with RMS values of residuals of 0.0029 m in northing and 0.0020 m in easting. Tests are also performed to determine the factor that limits the effective area of the Projection. Numéro de notice : A2020-516 Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1597490 date de publication en ligne : 08/04/2019 En ligne : https://doi.org/10.1080/00396265.2019.1597490 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95676 [article]

Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis / Batuhan Kilic in Cartography and Geographic Information Science, Vol 47 n° 4 (July 2020)  

Public [article]  inCartography and Geographic Information Science > Vol 47 n° 4 (July 2020) . - pp 336 - 349 Titre : Investigating the quality of reverse geocoding services using text similarity techniques and logistic regression analysis Type de document : Article/Communication Auteurs : Batuhan Kilic, Auteur ; Fatih Gülgen, Auteur Année de publication : 2020 Article en page(s) : pp 336 - 349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] exploration de texte [Termes descripteurs IGN] géocodage inverse [Termes descripteurs IGN] géocodage par adresse postale [Termes descripteurs IGN] logique binaire [Termes descripteurs IGN] qualité des données [Termes descripteurs IGN] régression [Termes descripteurs IGN] similitude sémantique Résumé : (auteur) Location, usually defined by postal address information or geographic coordinate values, is one of the leading themes in geography. Famous global mapping services such as ArcGIS Online, Bing Maps, Google Maps, or Yandex Maps can provide users with address information of any geographic coordinates using reverse geocoding. The accuracy of retrieved addresses is quite essential for a service user. Several researchers have evaluated the accuracy of the process based on the positional errors between the retrieved and actual addresses. This article proposes a different assessment based on text similarity algorithms. In this study, the authors examine the outcomes of 15 different text similarity algorithms by comparing them with the reference data. They benefit from the binary logistic regression to evaluate the results. At the end of the case study, they conclude that the soft-term frequency/inverse document frequency algorithm is the most appropriate to measure the quality of postal addresses of all tested services. The Jaccard algorithm also produces successful results only for Google and Bing Maps services. Moreover, the study allows the reader to assess the results of reverse geocoding derived from the global map platforms that serve in the test region. Numéro de notice : A2020-339 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2020.1746198 date de publication en ligne : 20/04/2020 En ligne : https://doi.org/10.1080/15230406.2020.1746198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95214 [article]

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Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax / Jose Morales in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1451 - 1474 Titre : Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax Type de document : Article/Communication Auteurs : Jose Morales, Auteur ; Alfred Stein, Auteur ; Johannes Flacke, Auteur ; Jaap Zevenbergen, Auteur Année de publication : 2020 Article en page(s) : pp 1451 - 1474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] analyse de la valeur [Termes descripteurs IGN] analyse syntaxique [Termes descripteurs IGN] cartographie statistique [Termes descripteurs IGN] estimation quantitative [Termes descripteurs IGN] évaluation foncière [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] Guatemala [Termes descripteurs IGN] krigeage [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] modèle conceptuel de données localisées [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] régression [Termes descripteurs IGN] système d'information foncière Résumé : (auteur) Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice. Numéro de notice : A2020-306 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725014 date de publication en ligne : 11/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95148 [article]

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A robust total Kalman filter algorithm with numerical evaluation / Sida Li in Survey review, vol 52 n° 373 (July 2020)  

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Counting of grapevine berries in images via semantic segmentation using convolutional neural networks / Laura Zabawa in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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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)  

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Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data / Rochelle Schneider dos Santos in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland / Mateusz Tomal in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

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Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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An integrated approach to registration and fusion of hyperspectral and multispectral images / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Fusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method / Yuedong Wang in Journal of geodesy, vol 94 n° 5 (May 2020)  

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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)  

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