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scientifique

Direct georeferencing of oblique and vertical imagery in different coordinate systems / H. Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 95 (September 2014) . - pp 122 – 133 Titre : Direct georeferencing of oblique and vertical imagery in different coordinate systems Type de document : Article/Communication Auteurs : H. Zhao, Auteur ; B. Zhang, Auteur ; C. Wu, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 122 – 133 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] géoréférencement direct [Termes IGN] implémentation (informatique) [Termes IGN] précision du positionnement [Termes IGN] reconstruction 3D [Termes IGN] semis de points [Termes IGN] visée oblique [Termes IGN] visée verticale Résumé : (Auteur) Reconstruction of 3D models through integrating vertical and oblique imagery has been studied extensively. For a 3D reconstruction, object point cloud coordinates could be calculated using direct georeferencing (DG) obtained from the direct orientation data of a GPS/INS system. This paper implemented DG approaches for vertical and oblique imagery in the earth centered earth fixed frame (e-frame), local tangent frame (l-frame), and map projection frame (p-frame), respectively. In the p-frame, the earth curvature correction formulas were derived through naturalizing oblique imagery to vertical imagery to achieve a high positioning precision. Five basic stereo-pair models for vertical and oblique imagery were simulated to verify the positioning accuracy of different frames. Simulation experiments showed that DG in the e-frame and l-frame of these five scenarios were rigorous, and no systematic errors were imported by the DG model as these frames are Cartesian. DG in the p-frame has obvious systematic errors which are aroused by the earth curvature and projection deformation unconformity in the vertical and horizontal directions. These errors, however, can be compensated effectively through correcting image coordinates of the oblique imagery by extending the standard image coordinate correction approach and the exterior orientation (EO) height term. After the correction, the absolute positioning error is lower than 1/20 GSD for simulation test-1. In the p-frame, the process is straightforward, and it is convenient for producing maps. For high accuracy DG, though, it is recommended to adopt e-frame or l-frame options. Numéro de notice : A2014-476 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74053 [article]

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vol 31 n° 21 - November 2010 - Population estimation using remote sensing and GIS technologies (Bulletin de International Journal of Remote Sensing IJRS) / L. Wang

Public [n° ou bulletin]  est un bulletin de International Journal of Remote Sensing IJRS / Remote sensing and photogrammetry society (1980 -)   Titre : vol 31 n° 21 - November 2010 - Population estimation using remote sensing and GIS technologies Type de document : Périodique Auteurs : L. Wang, Éditeur scientifique ; C. Wu, Éditeur scientifique ; Remote sensing and photogrammetry society, Auteur Année de publication : 2010 Importance : 404 p. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] démographie [Termes IGN] population [Termes IGN] répartition géographique [Termes IGN] système d'information géographique [Termes IGN] télédétection Numéro de notice : 080-201013 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=13733 [n° ou bulletin]

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Seasonal sensitivity analysis of impervious surface estimation with satellite imagery / C. Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 12 (December 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 12 (December 2007) . - pp 1393 - 1401 Titre : Seasonal sensitivity analysis of impervious surface estimation with satellite imagery Type de document : Article/Communication Auteurs : C. Wu, Auteur ; F. Yuan, Auteur Année de publication : 2007 Article en page(s) : pp 1393 - 1401 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse de sensibilité [Termes IGN] analyse des mélanges spectraux [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] Ohio (Etats-Unis) [Termes IGN] régression [Termes IGN] surface imperméable [Termes IGN] variation saisonnière Résumé : (Auteur) Numerous approaches have been developed to quantify the distribution of impervious surfaces using remote sensing technologies. Most of these approaches have been applied to data from a single time period, typically in the summer season (June to September). Presently, it is not clear whether there is an optimal time for impervious surface estimation with these methods. In this paper, the seasonal sensitivity of impervious surface estimation is examined. In particular, Landsat TM/ETM+ imagery for four different seasons has been acquired for the environs of Franklin County, Ohio. Two impervious surface estimation methods, spectral mixture analysis and regression modeling, are used to test for seasonal variations. Results indicate that the summer image provides better accuracy with the spectral mixture analysis method, while consistent accuracies are obtained for all four seasons with regression modeling. Copyright ASPRS Numéro de notice : A2007-543 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.73.12.1393 En ligne : https://doi.org/10.14358/PERS.73.12.1393 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28906 [article]

Incorporating remote sensing information in modelling house values: a regression tree approach / D. Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 72 n° 2 (February 2006)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 72 n° 2 (February 2006) . - pp 129 - 138 Titre : Incorporating remote sensing information in modelling house values: a regression tree approach Type de document : Article/Communication Auteurs : D. Yu, Auteur ; C. Wu, Auteur Année de publication : 2006 Article en page(s) : pp 129 - 138 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de la valeur [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] bati [Termes IGN] coefficient de corrélation [Termes IGN] erreur moyenne arithmétique [Termes IGN] habitat (urbanisme) [Termes IGN] image Landsat-ETM+ [Termes IGN] Milwaukee [Termes IGN] régression linéaire [Termes IGN] zone urbaine Résumé : (Auteur) This paper explores the possibility of incorporating remote sensing information in modeling house values in the City of Milwaukee, Wisconsin, U.S.A. In particular, a Landsat ETM+ image was utilized to derive environmental characteristics, including the fractions of vegetation, impervious surface, and soil, with a linear spectral mixture analysis approach. These environmental characteristics, together with house structural attributes, were integrated to house value models. Two modeling techniques, a global OLS regression and a regression tree approach, were employed to build the relationship between house values and house structural and environmental characteristics. Analysis of results indicates that environmental characteristics generated from remote sensing technologies have strong influences on house values, and the addition of them improves house value modeling performance significantly. Moreover, the regression tree model proves as a better alternative to the OLS regression models in terms of predicting accuracy. In particular, based on the testing dataset, the mean average error (MAE) and relative error (RE) dropped from 0.202 and 0.434 for the OLS model to 0.134 and 0.280 for the regression tree model, while the correlation coefficient between the predicted and observed values increased from 0.903 to 0.960. Further, as a nonparametric and local model, the regression tree method alleviates the problems with the OLS techniques and provides a means in delineating urban housing submarkets. Numéro de notice : A2006-037 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.14358/PERS.72.2.129 En ligne : https://doi.org/10.14358/PERS.72.2.129 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27764 [article]

Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery / C. Wu in Remote sensing of environment, vol 93 n° 4 (15/12/2004)  

Public [article]  inRemote sensing of environment > vol 93 n° 4 (15/12/2004) . - pp 480 - 492 Titre : Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery Type de document : Article/Communication Auteurs : C. Wu, Auteur Année de publication : 2004 Article en page(s) : pp 480 - 492 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] Colombus (Ohio) [Termes IGN] contrôle par télédétection [Termes IGN] image Landsat-ETM+ [Termes IGN] impact sur l'environnement [Termes IGN] milieu urbain [Termes IGN] surface imperméable [Termes IGN] utilisation du sol [Termes IGN] variable biophysique (végétation) [Termes IGN] végétation Résumé : (Auteur) With rapid urban growth in recent years, understanding urban biophysical composition and dynamics becomes an important research topic. Remote sensing technologies introduce a potentially scientific basis for examining urban composition and monitoring its changes over time. The vegetation-impervious surface-soil (V-I-S) model, in particular, provides a foundation for describing urban/suburban environments and a basis for further urban analyses including urban growth modeling, environmental impact analysis, and socioeconomic factor estimation. This paper develops a normalized spectral mixture analysis (NSMA) method to examine urban composition in Columbus (Ohio) using Landsat ETM+ data. In particular, a brightness normalization method is applied to reduce brightness variation. Through this normalization, brightness variability within each V-I-S component is reduced or eliminated, thus allowing a single endmember representing each component. Further, with the normalized image, three endmembers, vegetation, impervious surface, and soil, are chosen to model heterogeneous urban composition using a constrained spectral mixture analysis (SMA) model. The accuracy of impervious surface estimation is assessed and compared with two other existing models. Results indicate that the proposed model is a better alternative to existing models, with a root mean square error (RMSE) of 10.1% for impervious surface estimation in the study area. Numéro de notice : A2004-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.08.003 En ligne : https://doi.org/10.1016/j.rse.2004.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26981 [article]