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Geometric accuracy evaluation of YG-18 satellite imagery based on RFM / Ruishan Zhao in Photogrammetric record, vol 32 n° 157 (March - May 2017)
[article]
Titre : Geometric accuracy evaluation of YG-18 satellite imagery based on RFM Type de document : Article/Communication Auteurs : Ruishan Zhao, Auteur ; Yonghua Jiang, Auteur ; Guo Zhang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 33 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] estimation de précision
[Termes IGN] géoréférencement direct
[Termes IGN] image radar moirée
[Termes IGN] image YG
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] orthorectification automatique
[Termes IGN] point d'appui
[Termes IGN] précision géométrique (imagerie)
[Termes IGN] transformation affineRésumé : (auteur) YaoGan-18 (YG-18) is a Chinese high-resolution synthetic aperture radar (SAR) satellite, which was launched on 29th October 2013. So far, however, there have been no published reports evaluating the geometric accuracy of its imagery; this is rectified in this paper. A refined rational function model (RFM) with an affine transformation is utilised for the spaceborne SAR processing. By setting corner reflectors as ground control points (GCPs) and independent check points, the orthorectification accuracy of YG-18 satellite images was better than 2·5 m. Its stereo-SAR measurement accuracy was subsequently evaluated using a high-precision digital surface model (DSM), achieving an elevation accuracy of 11·36 m. The feasibility of the orthorectification method based on GCPs selected from corresponding image points of YG-18 SAR images and ZiYuan-3 (ZY-3) optical images was also verified, offering single-image accuracy better than 4·5 m. Numéro de notice : A2017-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12179 En ligne : http://dx.doi.org/10.1111/phor.12179 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84870
in Photogrammetric record > vol 32 n° 157 (March - May 2017) . - pp 33 - 47[article]The right imagery for the job / Charlotte Bishop in GEO: Geoconnexion international, vol 16 n° 3 (March 2017)
[article]
Titre : The right imagery for the job Type de document : Article/Communication Auteurs : Charlotte Bishop, Auteur Année de publication : 2017 Article en page(s) : pp 48 - 50 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] capteur spatial
[Termes IGN] image satellite
[Termes IGN] pouvoir de résolution géométriqueRésumé : (auteur) There has never been such an abundance of satellite imagery and elevation data. It can, however, pose challenges in knowing where to start in selecting the right imagery for the job. NPA Satellite Mapping’s Charlotte Bishop reviews the evolution of the market and the options now on offer. Numéro de notice : A2017-055 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84251
in GEO: Geoconnexion international > vol 16 n° 3 (March 2017) . - pp 48 - 50[article]Unsupervised object-based differencing for land-cover change detection / Jinxia Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 3 (March 2017)
[article]
Titre : Unsupervised object-based differencing for land-cover change detection Type de document : Article/Communication Auteurs : Jinxia Zhu, Auteur ; Yanjun Su, Auteur ; Qinghua Guo, Auteur ; Thomas C. Harmon, Auteur Année de publication : 2017 Article en page(s) : pp 225 - 236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme espérance-maximisation
[Termes IGN] altération
[Termes IGN] autocorrélation
[Termes IGN] changement d'occupation du sol
[Termes IGN] Chine
[Termes IGN] classification non dirigée
[Termes IGN] classification orientée objet
[Termes IGN] détection de changement
[Termes IGN] image multitemporelle
[Termes IGN] image SPOT-HRV
[Termes IGN] occupation du sol
[Termes IGN] traitement d'imageRésumé : (Auteur) One main problem of the spectral decomposition-based change detection method is the lack of efficient automatic techniques for developing the difference image. Traditional techniques generally assume that gray-level values in a difference image are independent and multitemporal images are co-registered/rectified perfectly without error. However, such assumptions are often violated because of the inevitable image misregistration and the interference of correlations between spectral bands. This study proposes an automated method based on the object-based multivariate alteration detection/maximum autocorrelation factor approach and the Gaussian mixture model-expectation maximization algorithm to obtain unsupervised difference images. This procedure is applied to bi-temporal (2005 and 2006) SPOT-HRV images at Panyu District Ponds, China. Results show that the proposed method successfully excludes the correlations of spectral bands and the influence of misregistration, as evidenced by a higher accuracy (up to 93.6 percent). These unique technical characteristics make this analytical framework suitable for detecting changes. Numéro de notice : A2017-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.3.225 En ligne : https://doi.org/10.14358/PERS.83.3.225 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84424
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 3 (March 2017) . - pp 225 - 236[article]Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models / Samira Ghorbani Nejad in Geocarto international, vol 32 n° 2 (February 2017)
[article]
Titre : Delineation of groundwater potential zones using remote sensing and GIS-based data-driven models Type de document : Article/Communication Auteurs : Samira Ghorbani Nejad, Auteur ; Fatemeh Falah, Auteur ; Mania Daneshfar, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 167 - 187 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] base de données topographiques
[Termes IGN] carte hydrogéologique
[Termes IGN] eau souterraine
[Termes IGN] géomorphologie locale
[Termes IGN] image satellite
[Termes IGN] Iran
[Termes IGN] modèle orienté objet
[Termes IGN] ressources en eau
[Termes IGN] système d'information géographique
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) The rapid increase in human population has increased the groundwater resources demand for drinking, agricultural and industrial purposes. The main purpose of this study is to produce groundwater potential map (GPM) using weights-of-evidence (WOE) and evidential belief function (EBF) models based on geographic information system in the Azna Plain, Lorestan Province, Iran. A total number of 370 groundwater wells with discharge more than 10 m3s−1were considered and out of them, 256 (70%) were randomly selected for training purpose, while the remaining114 (30%) were used for validating the model. In next step, the effective factors on the groundwater potential such as altitude, slope aspect, slope angle, curvature, distance from rivers, drainage density, topographic wetness index, fault distance, fault density, lithology and land use were derived from the spatial geodatabases. Subsequently, the GPM was produced using WOE and EBF models. Finally, the validation of the GPMs was carried out using areas under the ROC curve (AUC). Results showed that the GPM prepared using WOE model has the success rate of 73.62%. Similarly, the AUC plot showed 76.21% prediction accuracy for the EBF model which means both the models performed fairly good predication accuracy. The GPMs are useful sources for planners and engineers in water resource management, land use planning and hazard mitigation purpose. Numéro de notice : A2017-133 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1132481 Date de publication en ligne : 25/01/2016 En ligne : http://dx.doi.org/10.1080/10106049.2015.1132481 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85199
in Geocarto international > vol 32 n° 2 (February 2017) . - pp 167 - 187[article]Effect of training class label noise on classification performances for land cover mapping with satellite image time series / Charlotte Pelletier in Remote sensing, vol 9 n° 2 (February 2017)
[article]
Titre : Effect of training class label noise on classification performances for land cover mapping with satellite image time series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Claire Marais-Sicre, Auteur ; Gérard Dedieu, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données d'occupation du sol
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Landsat-8
[Termes IGN] image SPOT 4
[Termes IGN] série temporelleRésumé : (auteur) Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise. Numéro de notice : A2017-896 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : doi.org/10.3390/rs9020173 Date de publication en ligne : 18/02/2017 En ligne : https://doi.org/10.3390/rs9020173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91880
in Remote sensing > vol 9 n° 2 (February 2017) . - pp 1 - 24[article]Inconsistent estimates of forest cover change in China between 2000 and 2013 from multiple datasets: differences in parameters, spatial resolution, and definitions / Yan Li in Scientific reports, vol 7 (2017)PermalinkInferring spatial scale change in an isopleth map / J. Lin in Cartographic journal (the), Vol 54 n° 1 (February 2017)PermalinkA network-based enhanced spectral diversity approach for TOPS time-series analysis / Heresh Fattahi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)PermalinkObject-based water body extraction model using Sentinel-2 satellite imagery / Gordana Kaplan in European journal of remote sensing, vol 50 n° 1 (2017)PermalinkThe road from ruin / Philip Briscoe in GEO: Geoconnexion international, vol 16 n° 2 (February 2017)PermalinkAnalyse de séries temporelles d’images Sentinel et intégration de connaissances pour la classification en milieu agricole / Simon Bailly (2017)PermalinkPermalinkAutomatic production of large-scale cloud-free orthomosaics from multitemporal satellite images / Nicolas Champion (2017)PermalinkAutomatisation de l’acquisition et du traitement des images Sentinel-2 pour le calcul d’indices de végétation aidant à la prévention des pics de paludisme à Madagascar / Charlotte Wolff (2017)PermalinkCartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)Permalink