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Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods / Liheng Peng in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)
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Titre : Combining GF-2 and RapidEye satellite data for mapping mangrove species using ensemble machine-learning methods Type de document : Article/Communication Auteurs : Liheng Peng, Auteur ; Kai Liu, Auteur ; Jingjing Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 813 - 838 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] Chine, mer de
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] écosystème
[Termes IGN] extraction de la végétation
[Termes IGN] île
[Termes IGN] image Gaofen
[Termes IGN] image RapidEye
[Termes IGN] image satellite
[Termes IGN] mangrove
[Termes IGN] modèle numérique de surface
[Termes IGN] précision de la classification
[Termes IGN] Rotation Forest classificationRésumé : (auteur) Mangrove forests are important constitutions for sustainable development of coastal ecosystems, and they are often mapped and monitored with remote sensing approaches. Satellite images allow detailed studies of the distribution and composition of mangrove forests, and therefore facilitate the management and conservation of the ecosystems. The combination of multiple types of satellite images with different spatial and spectral resolutions is helpful in mangrove forests extraction and mangrove species discrimination as it reduces sampling workload and increases classification accuracies. In this study, the 1.0-m-resolution Gaofen-2 (GF-2) and the 5.0-m-resolution RapidEye-4 (RE-4) satellite images, acquired in February 2017 and November 2016 respectively, were used with ensemble machine-learning and object-oriented methods for mangroves mapping at both the community and species levels of the Qi’ao Island, Zhuhai, China. First, the mangroves on the island were segmented from the GF-2 image on a large scale, and then they were extracted combining with their digital elevation model (DEM) data. Second, the GF-2 image was further processed on a fine scale, in which object-oriented features from both the GF-2 and RE-4 images were extracted for each mangrove species. Third, it is followed by the mangrove species classification process which involves three ensemble machine-learning methods: the adaptive boosting (AdaBoost), the random forest (RF) and the rotation forest (RoF). These three methods employed a classification and regression tree (CART) as the base classifier. The results show that the overall accuracy (OA) of mangrove area extraction on the Qi’ao Island with the auxiliary data, DEM, achieves 98.76% (Kappa coefficient (κ) = 0.9289). The features extracted by the GF-2 and RE-4 images were shown to be beneficial for mangrove species discrimination. A maximum improvement in the OA of approximately 8% and a κκ of approximately 0.10 were achieved when employing RoF (OA = 92.01%, κ = 0.9016). Ensemble-learning methods can significantly improve the classification accuracy of CART, and the use of a bagging scheme (RF and RoF) is shown as a better way to map mangrove species than adaptive boosting (AdaBoost). In addition, RoF performed well in mangrove species classification but it was not as robust as the RF, whose average OA and κκ were 80.59% and 0.7608, respectively, while the RoF’s were 77.45% and 0.7214, respectively, in the 10-fold cross-validation. Numéro de notice : A2020-212 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1648907 Date de publication en ligne : 30/07/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648907 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94897
in International Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 813 - 838[article]Modelling the orthoimage accuracy using DEM accuracy and off-nadir angle / Altan Yilmaz in Geocarto international, Vol 35 n° 1 ([02/01/2020])
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Titre : Modelling the orthoimage accuracy using DEM accuracy and off-nadir angle Type de document : Article/Communication Auteurs : Altan Yilmaz, Auteur ; Mustafa Erdogan, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] angle nadiral
[Termes IGN] centrale inertielle
[Termes IGN] erreur
[Termes IGN] erreur moyenne quadratique
[Termes IGN] modèle empirique
[Termes IGN] modèle numérique de surface
[Termes IGN] orthoimage
[Termes IGN] planimétrie
[Termes IGN] point d'appuiRésumé : (auteur) Orthoimages are differentially rectified images that are corrected for the distortions caused especially by image tilt and topographic relief. The orientation, digital elevation model (DEM) and off-nadir angle plays an important role in orthoimage accuracy. The orientation error mostly occurs due to the quality and distribution of the ground control points. In this study, an attempt has been made to model the remaining errors by keeping the orientation error constant. To model the accuracy, orthoimages are produced with eight DEMs having different accuracies and are assessed using 50 check points. As the theoretical model cannot reflect the real world exactly, an empirical model is used for estimating the orthoimage accuracy. This proposed model was validated by another dataset. It is concluded that statistically there is no significant difference between the calculated model and real planimetric errors. The proposed model can be used in predicting orthoimage accuracy provided that the DEM accuracy and off-nadir angles of the points are known. Numéro de notice : A2020-016 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1493157 Date de publication en ligne : 12/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1493157 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94409
in Geocarto international > Vol 35 n° 1 [02/01/2020] . - pp 1 - 16[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2020011 RAB Livre Centre de documentation En réserve L003 Disponible Spatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland / Paweł Cybulski in Journal of maps, vol 16 n° 1 ([02/01/2020])
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Titre : Spatial visualization of quantitative landscape changes in an industrial region between 1827 and 1883. Case study Katowice, southern Poland Type de document : Article/Communication Auteurs : Paweł Cybulski, Auteur ; Lukasz Wielebski, Auteur ; Beata Medyńska-Gulij, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 77 - 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] 1:100.000
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte thématique
[Termes IGN] dégradation de l'environnement
[Termes IGN] détection de changement
[Termes IGN] dix-neuvième siècle
[Termes IGN] géoréférencement
[Termes IGN] paysage industriel
[Termes IGN] Pologne
[Termes IGN] prospection minérale
[Termes IGN] système d'information géographique
[Termes IGN] visualisation cartographiqueRésumé : (auteur) The aim of the study is to present landscape changes in the nineteenth century in the central part of the Upper Silesian Industrial District, which is the municipality of Katowice (southern Poland). The comparison of changes, particularly components of the geographical environment, is based on two time periods – the year 1827 and 1883. Nineteenth-century maps were georeferenced, digitized and a series of thematic spatial visualizations presenting quantitative changes were generated by means of the Geographic Information System (GIS). The scale of the visualization created is 1:100,000 and the area is 16,400 ha. The spatial visualization of quantitative landscape change shows the development of the anthropogenic pressure in the form of settlement areas, raw materials extraction places, roads, and the decrease of natural environments, such as forests, rivers, and water bodies. These changes were caused mainly by the exploration of underground deposits and the rapidly growing population of Upper Silesia. Numéro de notice : A2020-643 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2020.1746416 Date de publication en ligne : 04/04/2020 En ligne : https://doi.org/10.1080/17445647.2020.1746416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96069
in Journal of maps > vol 16 n° 1 [02/01/2020] . - pp 77 - 85[article]
Titre : 2014 Hartebeesthoek co-location survey reprocessing report Type de document : Rapport Auteurs : Jean-Michaël Muller , Auteur ; Damien Pesce, Auteur ; Xavier Collilieux
, Auteur
Mention d'édition : version 1 Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2020 Collection : Documents techniques du SGM num. 600 82 8678 Importance : 80 p. Format : 21 x 30 cm Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] antenne ITGB
[Termes IGN] axe de rotation de la Terre
[Termes IGN] géoréférencement
[Termes IGN] Hartebeesthoek
[Termes IGN] international GPS service for geodynamics
[Termes IGN] Johannesbourg
[Termes IGN] matrice de covariance
[Termes IGN] point de liaison (géodésie)Note de contenu : 1- Contexy
2- Georeferencing
3- HRAO determination
4- Axis determination
5- Axiscombination
6- Distance precision
7- Observations weights
8- Final results
9- References
ObservationsNuméro de notice : 28548 Affiliation des auteurs : IGN (2020- ) Thématique : POSITIONNEMENT Nature : Rapport nature-HAL : Rapport Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97409 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 28548-01 7D Livre SGM K001 Exclu du prêt Documents numériques
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2014 Hartebeesthoek... - pdf auteur -Adobe Acrobat PDF3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets / Hessah Albanwan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 1 (January 2020)
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Titre : 3D iterative spatiotemporal filtering for classification of multitemporal satellite data sets Type de document : Article/Communication Auteurs : Hessah Albanwan, Auteur ; Rongjun Qin, Auteur ; Xiaohu Lu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 23 - 31 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de données
[Termes IGN] changement d'occupation du sol
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification orientée objet
[Termes IGN] données multitemporelles
[Termes IGN] filtrage spatiotemporel
[Termes IGN] image à très haute résolution
[Termes IGN] itération
[Termes IGN] orthoimageRésumé : (Auteur) The current practice in land cover/land use change analysis relies heavily on the individually classified maps of the multi-temporal data set. Due to varying acquisition conditions (e.g., illumination, sensors, seasonal differences), the classification maps yielded are often inconsistent through time for robust statistical analysis. 3D geometric features have been shown to be stable for assessing differences across the temporal data set. Therefore, in this article we investigate the use of a multi-temporal orthophoto and digital surface model derived from satellite data for spatiotemporal classification. Our approach consists of two major steps: generating per-class probability distribution maps using the random-forest classifier with limited training samples, and making spatiotemporal inferences using an iterative 3D spatiotemporal filter operating on per-class probability maps. Our experimental results demonstrate that the proposed methods can consistently improve the individual classification results by 2%–6% and thus can be an important postclassification refinement approach. Numéro de notice : A2020-049 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.1.23 Date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.14358/PERS.86.1.23 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94534
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 1 (January 2020) . - pp 23 - 31[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2020011 SL Revue Centre de documentation Revues en salle Disponible PermalinkAnalyse automatique du couvert végétal pour la gestion du risque végétation en milieu ferroviaire à partir d'imagerie aérienne / Hélène Rouillon (2020)
PermalinkAnalyse, structuration et sémantisation des images aériennes [diaporama] / Valérie Gouet-Brunet (2020)
PermalinkPermalinkAssessment of ArcGIS based extraction of geoidal undulation compared to National Geospatial Intelligence Agency (NGA) model – A case study / Sher Muhammad in Journal of applied geodesy, vol 14 n° 1 (January 2020)
PermalinkAutomatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
PermalinkCamera 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)
PermalinkPermalinkDéveloppement de la photogrammétrie et d'analyses d'images pour l'étude et le suivi d'habitats marins / Guilhem Marre (2020)
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