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Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules / K. Germaine in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)
[article]
Titre : Delineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules Type de document : Article/Communication Auteurs : K. Germaine, Auteur ; M.C. Hung, Auteur Année de publication : 2011 Article en page(s) : pp 75 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification ISODATA
[Termes IGN] données lidar
[Termes IGN] image multibande
[Termes IGN] Nebraska (Etats-Unis)
[Termes IGN] précision de la classification
[Termes IGN] surface imperméable
[Termes IGN] système à base de connaissances
[Termes IGN] système expertRésumé : (Auteur) An attempt to delineate impervious surfaces in the City of Scottsbluff, Nebraska, was made using multispectral high spatial resolution imagery and lidar data. An isodata classification was performed and results aggregated into two parent classes, impervious and pervious. The ISODATA classification yielded an overall accuracy of 91.0 percent with a Kappa of 82.0 percent. A Knowledge Based Expert System (kbes) set of rules was designed incorporating the imagery classification with lidar data to derive two models, Cover Height and Cover Slope, to provide critical information not available from multispectral imagery. The rules were applied to the initial isodata classification to improve the classification accuracy to an overall accuracy of 94.0 percent with a Kappa of 87.9 percent. In this study, it was shown that lidar holds promise for improving the accuracy of impervious surface measurement, as well as the potential identification and measurement of other significant planimetric features such as buildings and trees. Numéro de notice : A2011-003 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.77.1.75 En ligne : https://doi.org/10.14358/PERS.77.1.75 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30785
in Photogrammetric Engineering & Remote Sensing, PERS > vol 77 n° 1 (January 2011) . - pp 75 - 85[article]A featureless approach to 3D polyhedral building modeling from aerial images / Karim Hammoudi in Sensors, vol 11 n° 1 (January 2011)
[article]
Titre : A featureless approach to 3D polyhedral building modeling from aerial images Type de document : Article/Communication Auteurs : Karim Hammoudi , Auteur ; Fadi Dornaika , Auteur Année de publication : 2011 Article en page(s) : pp 228 - 259 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image aérienne
[Termes IGN] luminance lumineuse
[Termes IGN] mesure de similitude
[Termes IGN] méthode robuste
[Termes IGN] orthoimage
[Termes IGN] reconstruction 3D du bâtiRésumé : (auteur) This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach. Numéro de notice : A2011-611 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s110100228 Date de publication en ligne : 28/12/2010 En ligne : https://doi.org/10.3390/s110100228 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91711
in Sensors > vol 11 n° 1 (January 2011) . - pp 228 - 259[article]A hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)
[article]
Titre : A hybrid classification scheme for mining multisource geospatial data Type de document : Article/Communication Auteurs : R. Vatsavai, Auteur ; B. Bhaduri, Auteur Année de publication : 2011 Article en page(s) : pp 29 - 47 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] classification hybride
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données auxiliaires
[Termes IGN] exploration de données géographiques
[Termes IGN] image Landsat
[Termes IGN] image multibande
[Termes IGN] précision de la classificationRésumé : (Auteur) Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of the large number of accurate training samples (10 to 30 * |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of the statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately, there is no convenient multivariate statistical model that can be employed for multisource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on Landsat satellite image datasets, and our new hybrid approach shows over 24% to 36% improvement in overall classification accuracy over conventional classification schemes. Numéro de notice : A2011-027 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-010-0113-4 Date de publication en ligne : 22/07/2010 En ligne : https://doi.org/10.1007/s10707-010-0113-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30808
in Geoinformatica > vol 15 n° 1 (January 2011) . - pp 29 - 47[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 057-2011011 RAB Revue Centre de documentation En réserve L003 Disponible Land cover classification of cloud-contaminated multitemporal high-resolution images / A. Salberg in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)
[article]
Titre : Land cover classification of cloud-contaminated multitemporal high-resolution images Type de document : Article/Communication Auteurs : A. Salberg, Auteur Année de publication : 2011 Article en page(s) : pp 377 - 387 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur non paramétrique
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] image multitemporelle
[Termes IGN] image optique
[Termes IGN] Norvège
[Termes IGN] occupation du solRésumé : (Auteur) We show how methods proposed in the statistical community dealing with missing data may be applied for land cover classification, where optical observations are missing due to clouds and snow. The proposed method is divided into two stages: 1) cloud/snow classification and 2) training and land cover classification. The purpose of the cloud/snow classification stage is to determine which pixels are missing due to clouds and snow. All pixels in each optical image are classified into the classes cloud, snow, water, and vegetation using a suitable classifier. The pixels classified as cloud or snow are labeled as missing, and this information is used in the subsequent training and classification stage, which deals with classification of the pixels into various land cover classes. For land cover classification, we apply the maximum-likelihood (assuming normal distributions), -nearest neighbor, and Parzen classifiers, all modified to handle missing features. The classifiers are evaluated on Landsat (both Thematic Mapper and Enhanced Thematic Mapper Plus) images covering a scene at about 900 m a.s.l. in the Hardangervidda mountain plateau in Southern Norway, where 4869 in situ samples of the land cover classes water, ridge, leeside, snowbed, mire, forest, and rock are obtained. The results show that proper modeling of the missing pixels improves the classification rate by 5%-10%, and by using multiple images, we increase the chance of observing the land cover type substantially. The nonparametric classifiers handle nonignorable missing-data mechanisms and are therefore particularly suitable for remote sensing applications where the pixels covered by snow and cloud may depend on the land cover type. Numéro de notice : A2011-052 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2052464 Date de publication en ligne : 26/07/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2052464 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30833
in IEEE Transactions on geoscience and remote sensing > vol 49 n° 1 Tome 2 (January 2011) . - pp 377 - 387[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2011011B RAB Revue Centre de documentation En réserve L003 Disponible
contenu dans Photogrammetric image analysis PIA 11, Munich, Germany, October 5-7, 2011 / Uwe Stilla (2011)
Titre : Multiscale Haar transform for blur estimation from a set of images Type de document : Article/Communication Auteurs : Lâmân Lelégard , Auteur ; Bruno Vallet , Auteur ; Mathieu Brédif , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2011 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 38-3/W22 Conférence : PIA 2011, ISPRS Conference on Photogrammetric Image Analysis 05/10/2011 07/10/2011 Munich Allemagne OA ISPRS Archives Importance : pp 65 - 70 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acuité stéréoscopique
[Termes IGN] contour
[Termes IGN] étalement radiométrique
[Termes IGN] étalonnage radiométrique
[Termes IGN] filtre de Gauss
[Termes IGN] flou
[Termes IGN] netteté
[Termes IGN] ondelette de Haar
[Termes IGN] orthophotoplan numérique
[Termes IGN] tâche image d'un pointRésumé : (auteur) This paper proposes a method to estimate the local sharpness of an optical system through the wavelet-based analysis of a large set of images it acquired. Assuming a space-invariant distribution of image features, such as in the aerial photography context, the proposed approach produces a sharpness map of the imaging device over 16x16 pixels blocks that enables, for instance, the detection of optical defects and the qualification of the mosaicking of multiple sensor images into a larger composite image. The proposed analysis is based on accumulating of the edge maps corresponding to the first levels of the Haar Transform of each image of the dataset, following the intuition that statistically, each pixel will see the same image structures. We propose a calibration method to transform these accumulated edge maps into a sharpness map by approximating the local PSF (Point Spread Function) with a Gaussian blur. Numéro de notice : C2011-036 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprsarchives-XXXVIII-3-W22-65-2011 Date de publication en ligne : 26/04/2013 En ligne : https://doi.org/10.5194/isprsarchives-XXXVIII-3-W22-65-2011 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92340 Documents numériques
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