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Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) / Wenzhi Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 237 - 250 Titre : Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM) Type de document : Article/Communication Auteurs : Wenzhi Zhao, Auteur ; Yanchen Bo, Auteur ; Jiage Chen, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 237 - 250 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classe sémantique [Termes IGN] compréhension de l'image [Termes IGN] fusion de données [Termes IGN] image à haute résolution [Termes IGN] reconnaissance d'objets [Termes IGN] scène urbaine Résumé : (Auteur) Urban scenes refer to city blocks which are basic units of megacities, they play an important role in citizens’ welfare and city management. Remote sensing imagery with largescale coverage and accurate target descriptions, has been regarded as an ideal solution for monitoring the urban environment. However, due to the heterogeneity of remote sensing images, it is difficult to access their geographical content at the object level, let alone understanding urban scenes at the block level. Recently, deep learning-based strategies have been applied to interpret urban scenes with remarkable accuracies. However, the deep neural networks require a substantial number of training samples which are hard to satisfy, especially for high-resolution images. Meanwhile, the crowed-sourced Open Street Map (OSM) data provides rich annotation information about the urban targets but may encounter the problem of insufficient sampling (limited by the places where people can go). As a result, the combination of OSM and remote sensing images for efficient urban scene recognition is urgently needed. In this paper, we present a novel strategy to transfer existing OSM data to high-resolution images for semantic element determination and urban scene understanding. To be specific, the object-based convolutional neural network (OCNN) can be utilized for geographical object detection by feeding it rich semantic elements derived from OSM data. Then, geographical objects are further delineated into their functional labels by integrating points of interest (POIs), which contain rich semantic terms, such as commercial or educational labels. Lastly, the categories of urban scenes are easily acquired from the semantic objects inside. Experimental results indicate that the proposed method has an ability to classify complex urban scenes. The classification accuracies of the Beijing dataset are as high as 91% at the object-level and 88% at the scene level. Additionally, we are probably the first to investigate the object level semantic mapping by incorporating high-resolution images and OSM data of urban areas. Consequently, the method presented is effective in delineating urban scenes that could further boost urban environment monitoring and planning with high-resolution images. Numéro de notice : A2019-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.019 Date de publication en ligne : 29/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92675 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2019051	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2019053	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2019052	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A robust fixed rank kriging method for improving the spatial completeness and accuracy of satellite SST products / Yuxin Zhu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 5021 - 5035 Titre : A robust fixed rank kriging method for improving the spatial completeness and accuracy of satellite SST products Type de document : Article/Communication Auteurs : Yuxin Zhu, Auteur ; Emily Lei Kang, Auteur ; Yanchen Bo, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5021 - 5035 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multirésolution [Termes IGN] changement climatique [Termes IGN] climat terrestre [Termes IGN] erreur systématique [Termes IGN] exhaustivité des données [Termes IGN] image Aqua-MODIS [Termes IGN] interpolation linéaire [Termes IGN] krigeage [Termes IGN] méthode robuste [Termes IGN] température de surface de la mer Résumé : (Auteur) Sea surface temperature (SST) plays a vital role in the Earth's atmosphere and climate systems. Complete and accurate SST observations are in great demand for forecasting tropical cyclones and projecting climate change. Satellite remote sensing has been used to retrieve SST globally, but missing values and biased observations impose difficulties on practical applications of these satellite-derived SST data. Conventional spatial statistics methods such as kriging have been widely used to fill the gaps. However, when such conventional methods are used to analyze a massive satellite data set of size n, the inversion of the n × n covariance matrix may require O(n3) computations, which make the computation very intensive or even infeasible. The fixed rank kriging (FRK) performs dimension reduction through multiresolution wavelet analysis so that it can dramatically reduce the computation cost of various kriging methods. However, the FRK cannot directly be used for incomplete data over spatially irregular regions such as SSTs, and the potential bias in the satellite data is not addressed. In this paper, we construct a data-driven bias-correction model for the correction of the bias in satellite SSTs and develop a robust FRK (R-FRK) method so that the dimension reduction can be used to the satellite data in irregular regions with missing data. We implement the bias-correction model and the R-FRK to the level-3 mapped night Moderate Resolution Imaging Spectroradiometer SSTs. The accuracy of the resulting predictions is assessed using the colocated drifting buoy SST observations, in terms of mean bias (bias), root-mean-squared error, and R squared (R2). The spatial completeness is assessed by the availability of ocean pixels. The assessment results show that the spatially. Numéro de notice : A2015-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2416351 Date de publication en ligne : 17/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2416351 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77558 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015091	SL	Revue	Centre de documentation	Revues en salle	Disponible