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Wireframing for interactive & web-based geographic visualization: designing the NOAA Lake Level Viewer / Robert Emmett Roth in Cartography and Geographic Information Science, Vol 44 n° 4 (July 2017)
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Titre : Wireframing for interactive & web-based geographic visualization: designing the NOAA Lake Level Viewer Type de document : Article/Communication Auteurs : Robert Emmett Roth, Auteur ; David Hart, Auteur ; Rashauna Mead, Auteur ; Chloë Quinn, Auteur Année de publication : 2017 Article en page(s) : pp 338 - 357 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Amérique du nord
[Termes IGN] géomatique web
[Termes IGN] géovisualisation
[Termes IGN] Grands Lacs
[Termes IGN] image NOAA
[Termes IGN] interface web
[Termes IGN] maquette fonctionnelle
[Termes IGN] niveau hydrostatique
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) In this article, we explore the potential of wireframe design and evaluation for interactive and web-based mapping through a case study on water level visualization. Specifically, our research informed design and development of the National Oceanic & Atmospheric Administration’s (NOAA) Lake Level Viewer (http://coast.noaa.gov/llv/), an interactive and web-based geovisualization application for the Great Lakes region of North America. As part of our overall user-centered design process, we created two sets of wireframes to evaluate two aspects of the user experience: high-fidelity wireframes to illustrate the proposed representation solution using real data and low-fidelity wireframes to provide a rough sketch of the proposed interaction solution. Eighteen target users completed cognitive walkthroughs of the wireframes, with the sessions audio-recorded for subsequent transcription and qualitative data analysis. The wireframe evaluations led to a series of revisions to the functional scope and visual design of the Lake Level Viewer. The process also generated recommendations for designing water level visualizations supporting adaptive management in response to climate change as well as for leveraging wireframes in support of large-scale mapping and GIS projects. Numéro de notice : A2017-225 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2016.1171166 En ligne : http://dx.doi.org/10.1080/15230406.2016.1171166 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85106
in Cartography and Geographic Information Science > Vol 44 n° 4 (July 2017) . - pp 338 - 357[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2017041 RAB Revue Centre de documentation En réserve L003 Disponible An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data / Michael Kwok-Po Ng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
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Titre : An adaptive weighted tensor completion method for the recovery of remote sensing images with missing data Type de document : Article/Communication Auteurs : Michael Kwok-Po Ng, Auteur ; Qiangqiang Yuan, Auteur ; Li Yan, Auteur ; Jing Sun, Auteur Année de publication : 2017 Article en page(s) : pp 3367 - 3381 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] bande spectrale
[Termes IGN] détection de partie cachée
[Termes IGN] données spatiotemporelles
[Termes IGN] image Aqua-MODIS
[Termes IGN] spectroradiométrie
[Termes IGN] tenseurRésumé : (Auteur) Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion. Numéro de notice : A2017-476 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2670021 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2670021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86401
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3367 - 3381[article]Can a machine generate humanlike language descriptions for a remote sensing image? / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
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Titre : Can a machine generate humanlike language descriptions for a remote sensing image? Type de document : Article/Communication Auteurs : Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2017 Article en page(s) : pp 3623 - 3634 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] descripteur
[Termes IGN] image à haute résolution
[Termes IGN] intelligence artificielle
[Termes IGN] interface en langage naturelRésumé : (Auteur) This paper investigates an intriguing question in the remote sensing field: “can a machine generate humanlike language descriptions for a remote sensing image?” The automatic description of a remote sensing image (namely, remote sensing image captioning) is an important but rarely studied task for artificial intelligence. It is more challenging as the description must not only capture the ground elements of different scales, but also express their attributes as well as how these elements interact with each other. Despite the difficulties, we have proposed a remote sensing image captioning framework by leveraging the techniques of the recent fast development of deep learning and fully convolutional networks. The experimental results on a set of high-resolution optical images including Google Earth images and GaoFen-2 satellite images demonstrate that the proposed method is able to generate robust and comprehensive sentence description with desirable speed performance. Numéro de notice : A2017-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2677464 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2677464 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86406
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3623 - 3634[article]Change detection in forests and savannas using statistical analysis based on geographical objects / Lucilia Rezende Leite in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)
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Titre : Change detection in forests and savannas using statistical analysis based on geographical objects Type de document : Article/Communication Auteurs : Lucilia Rezende Leite, Auteur ; Luis Marcelo Tavares de Carvalho, Auteur ; Fortunato Menezes da Silva, Auteur Année de publication : 2017 Article en page(s) : pp 284 - 295 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] Brésil
[Termes IGN] classification par la distance de Mahalanobis
[Termes IGN] détection de changement
[Termes IGN] forêt équatoriale
[Termes IGN] image Landsat-TM
[Termes IGN] khi carré
[Termes IGN] réflectance végétale
[Termes IGN] savane
[Termes IGN] segmentation d'imageRésumé : (auteur) The aim of this work was to assess techniques of land cover change detection in areas of Brazilian Forest and Savanna, using Landsat 5/TM images, and two iterative statistical methodologies based on geographical objects. The sensitivity of the methodologies was assessed in relation to the heterogeneity of the input data, the use of reflectance data and vegetation indices, and the use of different levels of confidence. The periods analyzed were from 2000 to 2006, and from 2006 to 2010. After the segmentation of images, the descriptive statistics average and standard deviation of each object were extracted. The determination of change objects was realized in an iterative way based on the Mahalanobis Distance and the chi-square distribution. The results were validated with an early visual detection and analyzed according to Receiver Operating Characteristic (ROC) Curve. Significant gains were obtained by using vegetation masks and bands 3 and 4 for both areas tested with 94,67% and 95,02% of the objects correctly detected as changes, respectively for the areas of Forest and Savanna. The use of the NDVI and different images were not satisfactory in this study. Numéro de notice : A2017-394 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1590/S1982-21702017000200018 En ligne : http://dx.doi.org/10.1590/S1982-21702017000200018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85910
in Boletim de Ciências Geodésicas > vol 23 n° 2 (abr - jun 2017) . - pp 284 - 295[article]Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis / Arati Paul in Geocarto international, vol 32 n° 6 (June 2017)
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Titre : Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis Type de document : Article/Communication Auteurs : Arati Paul, Auteur ; V.M. Chowdary, Auteur ; Y.K. Srivastava, Auteur ; Debsunder Dutta, Auteur ; J.R. Sharma, Auteur Année de publication : 2017 Article en page(s) : pp 640 - 654 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Calcutta
[Termes IGN] densité spectrale de puissance
[Termes IGN] détection de changement
[Termes IGN] détection de contours
[Termes IGN] Google Earth
[Termes IGN] image Cartosat-1
[Termes IGN] image IRS-LISS
[Termes IGN] occupation du sol
[Termes IGN] seuillage d'imageRésumé : (Auteur) Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively. Numéro de notice : A2017-275 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1167966 Date de publication en ligne : 01/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1167966 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85304
in Geocarto international > vol 32 n° 6 (June 2017) . - pp 640 - 654[article]Copernicus Sentinel-2A calibration and products validation status / Ferran Gascon in Remote sensing, vol 9 n° 6 (June 2017)
PermalinkDisplacement monitoring and modelling of a high-speed railway bridge using C-band Sentinel-1 data / Qihuan Huang in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkEffects of urban tree canopy loss on land surface temperature magnitude and timing / Arthur Elmes in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkEvaluation of forest fire on Madeira Island using Sentinel-2A MSI imagery / Gabriel Navarro in International journal of applied Earth observation and geoinformation, vol 58 (June 2017)
PermalinkIntegration of SSC TerraSAR-X images into multisource rapid mapping / D. Vassilaki in Photogrammetric record, vol 32 n° 158 (June - july 2017)
PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkPan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)
PermalinkTM-Based SOC models augmented by auxiliary data for carbon crediting programs in semi-arid environments / Salahuddin M. Jaber in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 6 (June 2017)
PermalinkInvestigating the potential of deep neural networks for large-scale classification of very high resolution satellite images / Tristan Postadjian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)
PermalinkAn unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)
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