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Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)
[article]
Titre : Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa Type de document : Article/Communication Auteurs : Kabir Yunus Peerbhay, Auteur ; Onisimo Mutanga, Auteur ; Riyad Ismail, Auteur Année de publication : 2013 Article en page(s) : pp 19 - 28 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse discriminante
[Termes IGN] arbre (flore)
[Termes IGN] classification dirigée
[Termes IGN] espèce végétale
[Termes IGN] forêt
[Termes IGN] image AISA+
[Termes IGN] image hyperspectrale
[Termes IGN] méthode des moindres carrésRésumé : (Auteur) Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies. Numéro de notice : A2013-231 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32369
in ISPRS Journal of photogrammetry and remote sensing > vol 79 (May 2013) . - pp 19 - 28[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013051 RAB Revue Centre de documentation En réserve L003 Disponible Support vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)
[article]
Titre : Support vector machine for spatial variation Type de document : Article/Communication Auteurs : C. Andris, Auteur ; D. Cowen, Auteur ; J. Wittenbach, Auteur Année de publication : 2013 Article en page(s) : pp 40 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse discriminante
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] exploration de données géographiques
[Termes IGN] seuillageRésumé : (Auteur) Large, multivariate geographic datasets have been used to characterize geographic space with the help of spatial data mining tools. In our study, we explore the sufficiency of the Support Vector Machine (SVM), a popular machine-learning technique for unsupervised classification and clustering, to help recognize hidden patterns in a college admissions dataset. Our college admissions dataset holds over 10,000 students applying to an undisclosed university during one undisclosed year. Students are qualified almost exclusively by their standardized test scores and school records, and a known admissions decision is rendered based on these criteria. Given that the university has a number of political, social and geographic econometric factors in its admissions decisions, we use SVM to find implicit spatial patterns that may favor students from certain geographic regions. We first explore the characteristics of the applicants in the college admissions case study. Next, we explain the SVM technique and our unique ‘threshold line’ methodology for both discrete (regional) and continuous (k-neighbors) space. We then analyze the results of the regional and k-neighbor tests in order to respond to the methodological and geographic research questions. Numéro de notice : A2013-039 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/j.1467-9671.2012.01354.x Date de publication en ligne : 09/10/2012 En ligne : https://doi.org/10.1111/j.1467-9671.2012.01354.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32177
in Transactions in GIS > vol 17 n° 1 (February 2013) . - pp 40 - 61[article]Contribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)
Titre : Contribution of texture and red-edge band for vegetated areas detection and identification Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; François Tassin, Auteur ; Nesrine Chehata , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2013 Conférence : IGARSS 2013, International Geoscience And Remote Sensing Symposium 21/07/2013 26/07/2013 Melbourne Australie Proceedings IEEE Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] bande rouge
[Termes IGN] classification
[Termes IGN] extraction de la végétation
[Termes IGN] feuillu
[Termes IGN] image aérienne
[Termes IGN] image optique
[Termes IGN] image proche infrarouge
[Termes IGN] image RapidEye
[Termes IGN] orthoimage
[Termes IGN] Pinophyta
[Termes IGN] texture d'imageRésumé : (auteur) High resolution GIS data describing forests is an important knowledge, both for mapping and for environmental monitoring purposes. The extraction of such information out of imagery consists in a detection of woody areas followed by a thematic enrichment in forested areas, including a discrimination between evergreen, deciduous and mixt plantings. This paper attempts to automatize these photo-interpretation tasks. It particularly emphasizes on the determination of the most suitable input data to cope with these two classification problems. Two kinds of optical images have indeed been used: RapidEye data and 50cm ground resolution aerial ortho-images. Aerial data provided very high resolution information and texture indices, whereas RapidEye data brought additional radiometric information, and especially a red-edge channel. It has then been shown that texture information from aerial 50cm images was a key information for the woody area detection and that the red-edge band of RapidEye data appeared to be useful to discriminate between evergreen and deciduous plantings. Numéro de notice : C2013-028 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2013.6723735 Date de publication en ligne : 27/01/2014 En ligne : https://doi.org/10.1109/IGARSS.2013.6723735 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80109 A new technique using infrared satellite measurements to improve the accuracy of the CALIPSO cloud-aerosol discrimination method / A. Naeger in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)
[article]
Titre : A new technique using infrared satellite measurements to improve the accuracy of the CALIPSO cloud-aerosol discrimination method Type de document : Article/Communication Auteurs : A. Naeger, Auteur ; S. Christopher, Auteur ; R. Ferrare, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 642 - 653 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aérosol
[Termes IGN] analyse discriminante
[Termes IGN] image Aqua-MODIS
[Termes IGN] image infrarouge
[Termes IGN] image MSG-SEVIRI
[Termes IGN] image Terra-MODIS
[Termes IGN] nuage
[Termes IGN] Sahara, désert du
[Termes IGN] température de luminance
[Termes IGN] tempête de poussièreRésumé : (Auteur) In this paper, we develop a new technique called the brightness temperature difference cloud and aerosol discrimination algorithm (BTD CAD) that uses thermal infrared satellite measurements to improve the accuracy of the cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) CAD algorithm. It has been shown that the CALIPSO CAD algorithm can misclassify dense dust as cloud because the CALIPSO two-wavelength backscatter lidar operates at 532 and 1064 nm where very similar scattering properties are known to exist between dense dust and cloud. Therefore, we use the 11 and 12 um thermal infrared channels from both the moderate resolution imaging spectroradiometer (MODIS) and the spinning enhanced visible and infrared imager (SEVIRI), which are very sensitive to dust concentration, in order to reduce the frequency of the dust misclassifications encountered by the CALIPSO CAD algorithm. For the two Saharan dust events presented in this paper, both the MODIS and SEVIRI BTD CAD techniques performed well but the MODIS BTD CAD correctly reclassified more CALIPSO CAD misclassifications as dust. After applying both techniques to all the daytime CALIPSO transects over North Africa during June 2007, the MODIS and SEVIRI BTD CAD increased the total number of detected aerosol layers by approximately 10% and 4%, respectively. Even though the Version 3 (V3) CAD algorithm is significantly more accurate in deciphering between dense dust and clouds than the Version 2 algorithm, the V3 still showed some dust misclassifications among the case studies. Thus, the BTD CAD technique can help reduce the frequency of dust misclassifications encountered by the V3 CAD algorithm. Numéro de notice : A2013-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2196437 En ligne : https://doi.org/10.1109/TGRS.2012.2196437 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32158
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 2 (January 2013) . - pp 642 - 653[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013011B RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised local discriminant analysis for feature extraction in hyperspectral images / W. Liao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)
[article]
Titre : Semisupervised local discriminant analysis for feature extraction in hyperspectral images Type de document : Article/Communication Auteurs : W. Liao, Auteur ; A. Pizurica, Auteur ; Paul Scheunders, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 184 - 198 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification semi-dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] matriceRésumé : (Auteur) We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both ill-posed and poor-posed conditions. The proposed method combines unsupervised methods (local linear feature extraction methods and supervised method (linear discriminant analysis) in a novel framework without any free parameters. The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination of the data inferred from the labeled samples. Experimental results on four real hyperspectral images demonstrate that the proposed method compares favorably with conventional feature extraction methods. Numéro de notice : A2013-013 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2200106 Date de publication en ligne : 28/06/2012 En ligne : https://doi.org/10.1109/TGRS.2012.2200106 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32151
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 1 Tome 1 (January 2013) . - pp 184 - 198[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013011A RAB Revue Centre de documentation En réserve L003 Disponible Tree species discrimination in tropical forests using airborne imaging spectroscopy / Jean-Baptiste Féret in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)PermalinkApports des données ALOS PALSAR polarimétriques à la détection des zones humides littorales (Sassandra, Côte d'Ivoire) / Kouakou Hervé Kouassi in Photo interprétation, European journal of applied remote sensing, vol 48 n° 4 (décembre 2012)PermalinkMapping malaria severity zones with Nigeriasat-1 incorporated into geographical information system / E. Ogunbadewa in Geocarto international, vol 27 n° 7 (November 2012)PermalinkClassification of urban tree species using hyperspectral imagery / R. Jensen in Geocarto international, vol 27 n° 5 (August 2012)PermalinkDétection et identification de zones de végétation arborée: utilisation conjointe d'images satellite RapidEye et de données BDOrtho / François Tassin (2012)PermalinkJoint processing of Landsat and ALOS-PALSAR data for forest mapping and monitoring / E. Lehmann in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)PermalinkPermalinkRobert Moffrat, Jr and his "Map of South Eastern Africa, 1848-51": Cartography in a time of uncertainty / Norman Etherington in Cartes & Géomatique, n° 210 (décembre 2011)PermalinkEmpirical comparison of full-waveform Lidar algorithms: range extraction and discrimination performance / C. Parrish in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 8 (August 2011)PermalinkDonnées géographiques / Pierre Dumolard (2011)Permalink