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Auteur J.A. Tullis |
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Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness / M.E. Hodgson in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)
[article]
Titre : Synergistic use of Lidar and color aerial photography for mapping urban parcel imperviousness Type de document : Article/Communication Auteurs : M.E. Hodgson, Auteur ; J.R. Jensen, Auteur ; J.A. Tullis, Auteur ; K.D. Riordan, Auteur ; C.M. Archer, Auteur Année de publication : 2003 Article en page(s) : pp 973 - 980 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] orthoimage
[Termes IGN] parcelle cadastrale
[Termes IGN] photographie en couleur
[Termes IGN] ruissellement
[Termes IGN] surface imperméable
[Termes IGN] système expertRésumé : (Auteur) The imperviousness of land parcels was mapped and evaluated using high spatial resolution digitized color orthophotography and surface-cover height extracted from multiple-return lidar data. Maximum-likelihood classification, spectral clustering, and expert system approaches were used to extract the impervious information from the datasets. Classified pixels (segments) were aggregated to parcels. The classification model based on the use of both the orthophotography and lidar-derived surface-cover height yielded impervious surface results for all parcels that were within 15 percent of reference data. The standard error for the rule-based per-pixel model was 7.15 percent with a maximum observed error of 18.94 percent. The maximum-likelihood per-pixel classification yielded a lower standard error of 6.62 percent with a maximum of 14.16 percent. The regression slope (i.e., 0. 955) for the maximum-likelihood per-pixel model indicated a near perfect relationship between observed and predicted imperviousness. The additional effort of using a per-segment approach with a rule-based classification resulted in slightly better standard error (5.85 percent) and a near-perfect regression slope (1.016). Numéro de notice : A2003-227 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.69.9.973 En ligne : https://doi.org/10.14358/PERS.69.9.973 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22522
in Photogrammetric Engineering & Remote Sensing, PERS > vol 69 n° 9 (September 2003) . - pp 973 - 980[article]Expert system house detection in high spatial resolution: Imagery using size, shape, and context / J.A. Tullis in Geocarto international, vol 18 n° 1 (March - May 2003)
[article]
Titre : Expert system house detection in high spatial resolution: Imagery using size, shape, and context Type de document : Article/Communication Auteurs : J.A. Tullis, Auteur ; J.R. Jensen, Auteur Année de publication : 2003 Article en page(s) : pp 5 - 15 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition de connaissances
[Termes IGN] analyse texturale
[Termes IGN] base de connaissances
[Termes IGN] détection du bâti
[Termes IGN] erreur de classification
[Termes IGN] exploration de données
[Termes IGN] fusion d'images
[Termes IGN] image à résolution métrique
[Termes IGN] image Ikonos
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] luminance lumineuse
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] photo-interprétation assistée par ordinateur
[Termes IGN] système expertRésumé : (Auteur) IKONOS 1 x 1 m panchromatic data fused with 4 X 4 m multispectral data were used for residential house detection in three 1 X 1 km study areas of Columbia, South Carolina. One study area contained houses built in the 1940s, another in the 1970s, and another in the 1990s. An expert system was developed to extract individual houses from the imagery. The system employed a data mining algorithm as the core of an automated knowledge acquisition module. Separate knowledge bases were generated for each study area using training samples and the data mining algorithm in two sequential stages. In the first stage, brightness values and NDVI yielded a knowledge base that was used to locate candidate house pixels. In the second stage, region metrics including size, shape, and a series of context variables were employed. Regions of asphalt roads mistakenly identified by the expert system as houses were removed using road buffers. Also, separate knowledge bases were generated both with and without the use of context variables. Each scenario was compared with a point map of photointerpreted (reference) houses. The photointerpreted database was verified against in situ housing counts. There was a strong increasing trend in both machine and photointerpreter accuracy as housing age decreased, with the highest accuracies (79 88%) in the 1990s study area. Numéro de notice : A2003-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040308542258 En ligne : https://doi.org/10.1080/10106040308542258 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22394
in Geocarto international > vol 18 n° 1 (March - May 2003) . - pp 5 - 15[article]Réservation
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