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Effect of data density, scan angle, and flying height on the accuracy of building extraction using LiDAR data / Bharat Lohani in Geocarto international, vol 23 n° 2 (April - May 2008)  

Public [article]  inGeocarto international > vol 23 n° 2 (April - May 2008) . - pp 81 - 94 Titre : Effect of data density, scan angle, and flying height on the accuracy of building extraction using LiDAR data Type de document : Article/Communication Auteurs : Bharat Lohani, Auteur ; Ranjit Singh, Auteur Année de publication : 2008 Article en page(s) : pp 81 - 94 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] angle de visée [Termes IGN] densité des points [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] hauteur de vol [Termes IGN] simulation 3D [Termes IGN] transformation de Hough Résumé : (Auteur) A Hough transform based approach for extraction of buildings using LiDAR data is presented. It is argued that LiDAR data should be smoothed and sparsed prior to Hough transform for better result. Algorithms to realize this are presented. Further, an algorithm which fits a vector model to extracted buildings is outlined. Simulated LiDAR data have been used to investigate the effect of three parameters (data density, flying height, and scan angle) on the quality of buildings extracted. A set of accuracy indices is proposed for this purpose. It is shown that the data density is the most significant parameter affecting the accuracy of building identification. Copyright Taylor & Francis Numéro de notice : A2008-077 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040701207100 En ligne : https://doi.org/10.1080/10106040701207100 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29072 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Mapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data / Z. Zhang in Geocarto international, vol 23 n° 2 (April - May 2008)  

Public [article]  inGeocarto international > vol 23 n° 2 (April - May 2008) . - pp 135 - 153 Titre : Mapping dominant vegetation communities at Meili Snow Mountain, Yunnan Province, China using satellite imagery and plant community data Type de document : Article/Communication Auteurs : Z. Zhang, Auteur ; E. De Clercq, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 135 - 153 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse spatiale [Termes IGN] carte de la végétation [Termes IGN] classification bayesienne [Termes IGN] classification par maximum de vraisemblance [Termes IGN] flore locale [Termes IGN] Kappa de Cohen [Termes IGN] milieu naturel [Termes IGN] montagne [Termes IGN] occupation du sol [Termes IGN] répartition géographique [Termes IGN] Yunnan (Chine) Résumé : (Auteur) Mapping dominant vegetation communities is important work for vegetation scientists. It is very difficult to map dominant vegetation communities using multispectral remote sensing data only, especially in mountain areas. However plant community data contain useful information about the relationships between plant communities and their environment. In this paper, plant community data are linked with remote sensing to map vegetation communities. The Bayesian soft classifier was used to produce posterior probability images for each class. These images were used to calculate the prior probabilities. One hundred and eighty plant plots at Meili Snow Mountain, Yunnan Province, China were used to characterize the vegetation distribution for each class along altitude gradients. Then, the frequencies were used to modify the prior probabilities of each class. After stratification in a vegetation part and a non-vegetation part, a maximum-likelihood classification with equal prior probabilities was conducted, yielding an overall accuracy of 82.1% and a kappa accuracy of 0.797. Maximum-likelihood classification with modified prior probabilities in the vegetation part, conducted with a conventional maximum-likelihood classification for the non-vegetation part, yielded an overall accuracy of 87.7%, and a kappa accuracy of 0.861. Copyright Taylor & Francis Numéro de notice : A2008-078 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040701337410 En ligne : https://doi.org/10.1080/10106040701337410 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29073 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

ISRV: an improved synthetic variable ratio method for image fusion / L. Wang in Geocarto international, vol 23 n° 2 (April - May 2008)  

Public [article]  inGeocarto international > vol 23 n° 2 (April - May 2008) . - pp 155 - 165 Titre : ISRV: an improved synthetic variable ratio method for image fusion Type de document : Article/Communication Auteurs : L. Wang, Auteur ; X. Cao, Auteur ; J. Chen, Auteur Année de publication : 2008 Article en page(s) : pp 155 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] affinage d'image [Termes IGN] classification dirigée [Termes IGN] ENVI [Termes IGN] fusion d'images [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] image panchromatique Résumé : (Auteur) An Improved Synthetic Variable Ratio (ISVR) fusion method is proposed to merge high spatial resolution panchromatic (Pan) images and multispectral (MS) images based on a simulation of the panchromatic image from the multispectral bands. Compared to the existing SVR (Synthetic Variable Ratio) family methods, the ISVR method manifests two major improvements: a simplified and physically meaningful scheme to derive the parameters necessary as required by SVR, and less computing power. Two sets of IKONOS Pan and MS images: one in urban area and another one in a forest area, were used to evaluate the effectiveness of classification-oriented ISVR method in comparison to the Principal Component Substitution (PCS), Synthetic Variable Ratio (SVR) and Gram-Schmidt Spectral Sharpening (GS) methods that are available in the ENVI software package. Results indicate the ISVR method achieves the best spectral fidelity to facilitate classification compared to PCS, SVR, and GS methods. Copyright Taylor & Francis Numéro de notice : A2008-079 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040701204198 En ligne : https://doi.org/10.1080/10106040701204198 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29074 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible