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Auteur Giles M. Foody |
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Integrating user needs on misclassification error sensitivity into image segmentation quality assessment / Hugo Costa in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 6 (June 2015)
[article]
Titre : Integrating user needs on misclassification error sensitivity into image segmentation quality assessment Type de document : Article/Communication Auteurs : Hugo Costa, Auteur ; Giles M. Foody, Auteur ; Doreen S. Boyd, Auteur Année de publication : 2015 Article en page(s) : pp 451 - 459 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des besoins
[Termes IGN] classification dirigée
[Termes IGN] connaissance thématique
[Termes IGN] objet géographique
[Termes IGN] occupation du sol
[Termes IGN] segmentation d'image
[Termes IGN] similitude
[Termes IGN] utilisateurRésumé : (auteur) Commonly the assessment of the quality of image segmentations used in object-based land cover classification uses the geometric match between the derived segmentation and a reference dataset. This paper argues that a more appropriate assessment of a segmentation is to also consider the thematic content of the objects generated. This allows the assessment to be tailored to the needs of the specific user. A new method for image segmentation quality assessment is described, which combines a traditional geometric-only method with the thematic similarity index (TSI), a metric that expresses the degree of thematic quality of objects from a user’s perspective. The perspectives of two users (a wolf researcher and a general user of land cover information) were adopted in a case study to demonstrate the new method. The results show that the new method allowed the production of more accurate land cover classifications for the two users than the use of the geometric-only approach Numéro de notice : A2015-976 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.6.451 En ligne : https://doi.org/10.14358/PERS.81.6.451 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80059
in Photogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 6 (June 2015) . - pp 451 - 459[article]Assessing the accuracy of Volunteered Geographic Information arising from multiple contributors to an Internet based collaborative project / Giles M. Foody in Transactions in GIS, vol 17 n° 6 (December 2013)
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Titre : Assessing the accuracy of Volunteered Geographic Information arising from multiple contributors to an Internet based collaborative project Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur ; Steffen Fritz, Auteur ; Linda M. See, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 847 - 860 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] acquisition de données
[Termes IGN] cartographie collaborative
[Termes IGN] données localisées des bénévoles
[Termes IGN] information géographique numérique
[Termes IGN] participation du public
[Termes IGN] précision des données
[Termes IGN] qualité des donnéesRésumé : (Auteur) The recent rise of neogeography and citizen sensing has increased the opportunities for the use of crowdsourcing as a means to acquire data to support geographical research. The value of the resulting volunteered geographic information is, however, often limited by concerns associated with its quality and the degree to which the contributing data sources may be trusted. Here, information on the quality of sources of volunteered geographic information was derived using a latent class analysis. The volunteered information was on land cover interpreted visually from satellite sensor images and the main focus was on the labeling of 299 sites by seven of the 65 volunteers who contributed to an Internet-based collaborative project. Using the information on land cover acquired by the multiple volunteers it was shown that the relative, but not absolute, quality of the data from different volunteers could be characterized accurately. Additionally, class-specific variations in the quality of the information provided by a single volunteer could be characterized by the analysis. The latent class analysis, therefore, was able to provide information on the quality of information provided on an inter- and intra-volunteer basis. Numéro de notice : A2013-672 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12033 Date de publication en ligne : 22/05/2013 En ligne : https://doi.org/10.1111/tgis.12033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32808
in Transactions in GIS > vol 17 n° 6 (December 2013) . - pp 847 - 860[article]Latent class modeling for site- and non-site-specific classification accuracy assessment without ground data / Giles M. Foody in IEEE Transactions on geoscience and remote sensing, vol 50 n° 7 Tome 2 (July 2012)
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Titre : Latent class modeling for site- and non-site-specific classification accuracy assessment without ground data Type de document : Article/Communication Auteurs : Giles M. Foody, Auteur Année de publication : 2012 Article en page(s) : pp 2827 - 2838 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique
[Termes IGN] estimation de précision
[Termes IGN] modèle de classe latente
[Termes IGN] précision de la classificationRésumé : (Auteur) Accuracy assessment should be a fundamental component of an image classification analysis and is typically undertaken following either a non-site- or a site-specific methodology. The assessment of classification accuracy is, however, often difficult, with many challenges associated with the ground data typically required. Using a series of classifications of two test sites, this paper shows that accuracy assessment from both perspectives is possible through the use of a latent class modeling approach in the absence of ground data. This is possible because the parameters of a latent class model that explains the observed associations in class labeling made by a series of classifications provide estimates of class cover and conditional probabilities of class membership that equate to popular non-site- and site-specific (producer's accuracy) measures of accuracy, respectively. Additionally, the latent class model provides a new classification that could be evaluated by traditional means if ground data are available. The classification of each test site derived from the latent class model was accurate, being of equivalent accuracy to a conventional ensemble classification that was based on the same series of classifications for a site. The ability to derive a highly accurate classification and yield estimates of classification accuracy without ground data to form a testing set indicates the considerable promise of the method and a means to reduce demands for costly ground data that may also be a source of error due to imperfections. Numéro de notice : A2012-321 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2174156 Date de publication en ligne : 19/12/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2174156 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31767
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 7 Tome 2 (July 2012) . - pp 2827 - 2838[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012071B RAB Revue Centre de documentation En réserve L003 Disponible Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions / M. Cutler in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)
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Titre : Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions Type de document : Article/Communication Auteurs : M. Cutler, Auteur ; D. Boyd, Auteur ; Giles M. Foody, Auteur ; A. Vetrivel, Auteur Année de publication : 2012 Article en page(s) : pp 66 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] analyse texturale
[Termes IGN] biomasse
[Termes IGN] biomasse (combustible)
[Termes IGN] Brésil
[Termes IGN] classification par réseau neuronal
[Termes IGN] déboisement
[Termes IGN] forêt tropicale
[Termes IGN] image JERS
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image radar
[Termes IGN] Malaisie
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] ondelette
[Termes IGN] texture d'image
[Termes IGN] ThaïlandeRésumé : (Auteur) Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). Numéro de notice : A2012-289 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31735
in ISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 66 - 77[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2012041 SL Revue Centre de documentation Revues en salle Disponible vol 30 n° 20 - October 2009 - Accuracy 2008, [actes], spatial accuracy in remote sensing, Shangai, 25 - 27 June 2008 (Bulletin de International Journal of Remote Sensing IJRS) / Jingxiong ZhangExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 080-09121 RAB Revue Centre de documentation En réserve L003 Exclu du prêt Training set size requirements for the classification of a specific class / Giles M. Foody in Remote sensing of environment, vol 104 n° 1 (15/09/2006)PermalinkLocalized soft classification for super-resolution mapping of the shoreline / Aidy M. Muslim in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)PermalinkThematic map comparison: evaluating the statistical significance of differences in classification accuracy / Giles M. Foody in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 5 (May 2004)PermalinkGeographical weighting as a further refinement to regression modelling: an example focused on the NDVI-rainfall relationship / Giles M. Foody in Remote sensing of environment, vol 88 n° 3 (15/12/2003)PermalinkEnvironmental remote sensing from regional to global scales / Giles M. Foody (1994)PermalinkMulti-temporal airborne synthetic aperture radar data for crop classification / Giles M. Foody in Geocarto international, vol 4 n° 3 (September - November 1989)PermalinkAnalysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths / T.F. Wood in International Journal of Remote Sensing IJRS, vol 10 n° 1 (January 1989)PermalinkThe effects of viewing geometry on image classification / Giles M. Foody in International Journal of Remote Sensing IJRS, vol 9 n° 12 (December 1988)PermalinkCrop classification from airborne synthetic aperture radar data / Giles M. Foody in International Journal of Remote Sensing IJRS, vol 9 n° 4 (April 1988)PermalinkRadiometric balancing : a comment / Giles M. Foody in International Journal of Remote Sensing IJRS, vol 8 n° 6 (June 1987)Permalink