Descripteur
Documents disponibles dans cette catégorie (127)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)
[article]
Titre : Combining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation Type de document : Article/Communication Auteurs : Aaron E. Maxwell, Auteur ; Thimoty A. Warner, Auteur ; Michael P. Strager, Auteur Année de publication : 2014 Article en page(s) : pp 179 - 189 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse combinatoire (maths)
[Termes IGN] données lidar
[Termes IGN] image RapidEye
[Termes IGN] mine
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] séparateur à vaste marge
[Termes IGN] visualisation cartographiqueRésumé : (Auteur) The combination of RapidEye satellite imagery and light detection and ranging (lidar) derivatives was assessed for mapping land-cover within a mountaintop coal surface mine complex in the southern coalfields of West Virginia, USA. Support vector machines (SVM), random forests (RF), and boosted classification and regression trees (CART) algorithms were used. Incorporation of the lidar-derived data increased map accuracy in comparison to using only the five imagery bands, and SVM generally produced a more accurate classification than the ensemble tree algorithms based on overall map accuracy, Kappa statistics, allocation disagreement, quantity disagreement, and McNemar's test of statistical significance. Based on measures of predictor variable importance within the ensemble tree classifiers, the normalized digital surface model (nDSM) was found to be more useful than first return intensity data for differentiating the classes Numéro de notice : A2014-111 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.2.179-189 En ligne : https://doi.org/10.14358/PERS.80.2.179-189 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33016
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 2 (February 2014) . - pp 179 - 189[article]Texture augmented detection of macrophyte species using decision trees / Cameron Proctor in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)
[article]
Titre : Texture augmented detection of macrophyte species using decision trees Type de document : Article/Communication Auteurs : Cameron Proctor, Auteur ; Yuhong He, Auteur ; Vincent Robinson, Auteur Année de publication : 2013 Article en page(s) : pp 10 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algue
[Termes IGN] classification par arbre de décision
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] macrophyte
[Termes IGN] précision de la classification
[Termes IGN] rivière
[Termes IGN] séparabilité
[Termes IGN] texture d'imageRésumé : (Auteur) Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 x 5 and 13 x 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier. Numéro de notice : A2013-295 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.022 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.02.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32433
in ISPRS Journal of photogrammetry and remote sensing > vol 80 (June 2013) . - pp 10 - 20[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013061 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral band clustering and band selection for urban land cover classification / H. Su in Geocarto international, vol 27 n° 5 (August 2012)
[article]
Titre : Hyperspectral band clustering and band selection for urban land cover classification Type de document : Article/Communication Auteurs : H. Su, Auteur ; Q. Du, Auteur Année de publication : 2012 Article en page(s) : pp 39 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] signature spectrale
[Termes IGN] valeur aberranteRésumé : (Auteur) The aim of this study is to combine band clustering with band selection for dimensionality reduction of hyperspectral imagery. The performance of dimensionality reduction is evaluated through urban land cover classification accuracy with the dimensionality-reduced data. Different from unsupervised clustering using all the pixels or supervised clustering requiring labelled pixels, the discussed semi-supervised band clustering needs class spectral signatures only; band selection result is used as initial condition for band clustering; after clustering, a cluster selection step is applied to select clusters to be used in the following data analysis. In this article, we propose to conduct band selection by removing outlier bands in each cluster before finalizing cluster centres. The experimental results in urban land cover classification show that the proposed algorithm can further enhance support vector machine (SVM)-based classification accuracy. Numéro de notice : A2012-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.643322 Date de publication en ligne : 12/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.643322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31816
in Geocarto international > vol 27 n° 5 (August 2012) . - pp 39 - 411[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2012051 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
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]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2012071B RAB Revue Centre de documentation En réserve L003 Disponible Urban tree cover mapping with relief-corrected aerial imagery and lidar / B. Lehrbass in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 5 (May 2012)
[article]
Titre : Urban tree cover mapping with relief-corrected aerial imagery and lidar Type de document : Article/Communication Auteurs : B. Lehrbass, Auteur ; Jing Wang, Auteur Année de publication : 2012 Article en page(s) : pp 473 - 484 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre (flore)
[Termes IGN] arbre urbain
[Termes IGN] carte de la végétation
[Termes IGN] classification orientée objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] flore urbaine
[Termes IGN] fusion d'images
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] Ontario (Canada)
[Termes IGN] orthoimage
[Termes IGN] précision de la classification
[Termes IGN] zone urbaineRésumé : (Auteur) Urban tree canopy cover is often mapped by classifying high-resolution multispectral imagery. However, it can be difficult to differentiate low-lying vegetation from tree cover using optical data alone. Combining a lidar-derived Normalized Digital Surface Model (ndsm) improves classification accuracy, but the optical imagery is often imperfectly aligned with the NDSM. Aerial imagery is normally orthorectified using the ground elevation. However, tall objects in the orthorectified imagery still suffer from relief displacement. This can cause classification errors when lidar and the aerial imagery are combined. This study presents an approach for urban tree cover mapping composed of two parts: a method for correcting the relief displacement of trees in previously orthorectified aerial imagery, and an object-based classification method which combines relief-corrected multispectral aerial imagery with a lidar-derived NDSM. Using these methods, the tree cover was mapped for a 1,600 ha region of London, Ontario, Canada with improved positional and classification accuracy. Numéro de notice : A2012-233 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.5.473 En ligne : https://doi.org/10.14358/PERS.78.5.473 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31679
in Photogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 5 (May 2012) . - pp 473 - 484[article]An edge-oriented approach to thematic map error assessment / S. Sweeney in Geocarto international, vol 27 n° 1 (February 2012)PermalinkAn assessment of internal neural network parameters affecting image classification accuracy / L. Zhou in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 12 (December 2011)PermalinkDevelopment of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)PermalinkHistorical land use as a feature for image classification / Jorge Abel Recio in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)PermalinkDelineation 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)PermalinkA hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)PermalinkRule-based classification of a very high resolution image in an urban environment using multispectral segmentation by cartographic data / M. Bouziani in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)PermalinkRange and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies / Ilkka Korpela in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 4 (July - August 2010)PermalinkInfluence of resolution in irrigated area mapping and area estimations / N. Velpuri in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 12 (December 2009)PermalinkContribution of airborne full-waveform Lidar and image data for urban scene classification / Nesrine Chehata (07/11/2009)Permalink