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Shoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method / Audrey Minghelli in Remote sensing, vol 12 n° 16 (August-2 2020)
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Titre : Shoreline extraction from WorldView2 satellite data in the presence of foam pixels using multispectral classification method Type de document : Article/Communication Auteurs : Audrey Minghelli, Auteur ; Jérôme Spagnoli, Auteur ; Manchun Lei , Auteur ; Malik Chami, Auteur ; Sabine Charmasson, Auteur
Année de publication : 2020 Projets : AMORAD / Radakovitch, Olivier Article en page(s) : n° 2664 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] baie
[Termes IGN] classification multibande
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection de contours
[Termes IGN] écume
[Termes IGN] image Worldview
[Termes IGN] littoral
[Termes IGN] Sendaï
[Termes IGN] trait de côteRésumé : (auteur) Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images ( Numéro de notice : A2020-570 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12162664 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.3390/rs12162664 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95877
in Remote sensing > vol 12 n° 16 (August-2 2020) . - n° 2664[article]SemCity Toulouse: a benchmark for building instance segmentation in satellite images / Ribana Roscher in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-5-2020 (August 2020)
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Titre : SemCity Toulouse: a benchmark for building instance segmentation in satellite images Type de document : Article/Communication Auteurs : Ribana Roscher, Auteur ; Michele Volpi, Auteur ; Clément Mallet , Auteur ; Lukas Drees, Auteur ; Jan Dirk Wegner, Auteur
Année de publication : 2020 Projets : 1-Pas de projet / Radakovitch, Olivier Conférence : ISPRS 2020, Commission 5, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 5 Article en page(s) : pp 109 - 116 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] bati
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] instance
[Termes IGN] Toulouse
[Termes IGN] zone urbaine denseRésumé : (auteur) In order to reach the goal of reliably solving Earth monitoring tasks, automated and efficient machine learning methods are necessary for large-scale scene analysis and interpretation. A typical bottleneck of supervised learning approaches is the availability of accurate (manually) labeled training data, which is particularly important to train state-of-the-art (deep) learning methods. We present SemCity Toulouse, a publicly available, very high resolution, multi-spectral benchmark data set for training and evaluation of sophisticated machine learning models. The benchmark acts as test bed for single building instance segmentation which has been rarely considered before in densely built urban areas. Additional information is provided in the form of a multi-class semantic segmentation annotation covering the same area plus an adjacent area 3 times larger. The data set addresses interested researchers from various communities such as photogrammetry and remote sensing, but also computer vision and machine learning. Numéro de notice : A2020-503 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-5-2020-109-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-5-2020-109-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95639
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-5-2020 (August 2020) . - pp 109 - 116[article]Breaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? / Izabela Golebiowska in Cartographic journal (the), Vol 57 n° 3 (August 2020)
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Titre : Breaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? Type de document : Article/Communication Auteurs : Izabela Golebiowska, Auteur ; Tomasz Opach, Auteur ; Jan Ketil Rød, Auteur Année de publication : 2020 Article en page(s) : pp 235 - 248 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle
[Termes IGN] comportement
[Termes IGN] exploration de données géographiques
[Termes IGN] interactivité
[Termes IGN] oculométrie
[Termes IGN] utilisateur
[Termes IGN] zone d'intérêt
[Vedettes matières IGN] GéovisualisationRésumé : (auteur) Maps are frequently combined with data displays in the form of coordinated and multiple views (CMV). Although CMV are valuable geovisualization tools, novice users may find them complex and thus require explanation. However, no tutorial guidelines have been developed that indicate what is helpful in understanding CMV geovisualization tools. We therefore conducted a study on the learnability of a CMV tool, informed with eye-tracking data, talk-aloud and interaction logs. We have investigated how untrained users work with a CMV geovisualization tool. The study revealed that: (1) despite their initial confusion, users found the tested tool pleasant to play with while getting to grips with how dynamic brushing works, (2) when examining the tool’s interface, participants mainly looked freely at explanatory elements, such as labels and the legend, but they explored interactive techniques only to a limited degree. We conclude with tips about tutorial design and layout design for CMV tools. Numéro de notice : A2020-805 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2019.1660513 Date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1080/00087041.2019.1660513 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96764
in Cartographic journal (the) > Vol 57 n° 3 (August 2020) . - pp 235 - 248[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 030-2020031 RAB Revue Centre de documentation En réserve L003 Disponible Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])
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Titre : Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method Type de document : Article/Communication Auteurs : Vijendra Singh Bramhe, Auteur ; Sanjay Kumar Ghosh, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2020 Article en page(s) : pp 1067 - 1087 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] analyse texturale
[Termes IGN] bati
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] réseau neuronal artificiel
[Termes IGN] texture d'imageRésumé : (auteur) Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively. Numéro de notice : A2020-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1566406 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566406 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95489
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087[article]Extraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])
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Titre : Extraction of urban built-up areas from nighttime lights using artificial neural network Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Giovanni Coco, Auteur ; Jay Gao, Auteur Année de publication : 2020 Article en page(s) : pp 1049 - 1066 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aménagement du territoire
[Termes IGN] bati
[Termes IGN] cartographie urbaine
[Termes IGN] classification dirigée
[Termes IGN] développement durable
[Termes IGN] échantillonnage
[Termes IGN] éclairage public
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] rayonnement lumineux
[Termes IGN] réseau neuronal artificiel
[Termes IGN] seuillage
[Termes IGN] température au sol
[Termes IGN] zone urbaineRésumé : (auteur) The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL. Numéro de notice : A2020-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559887 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559887 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95488
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1049 - 1066[article]Guided feature matching for multi-epoch historical image blocks pose estimation / Lulin Zhang in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
PermalinkNear-real time forecasting and change detection for an open ecosystem with complex natural dynamics / Jasper A. Slingsby in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkRecent changes in two outlet glaciers in the Antarctic Peninsula using multi-temporal Landsat and Sentinel-1 data / Carolina L. Simões in Geocarto international, vol 35 n° 11 ([01/08/2020])
PermalinkStructure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)
PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)
PermalinkEvaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. Hamylton in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)
PermalinkA novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
PermalinkSemi-automatic identification of submarine pipelines with synthetic aperture sonar Images / Victor Hugo Fernandes in Marine geodesy, Vol 43 n° 4 (July 2020)
PermalinkSimulating urban land use change by integrating a convolutional neural network with vector-based cellular automata / Yaqian Zhai in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)
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