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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 Assessing reference dataset representativeness through confidence metrics based on information density / Giorgos Mountrakis in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)
[article]
Titre : Assessing reference dataset representativeness through confidence metrics based on information density Type de document : Article/Communication Auteurs : Giorgos Mountrakis, Auteur ; Bo Xi, Auteur Année de publication : 2013 Article en page(s) : pp 129 - 147 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de sensibilité
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte de confiance
[Termes IGN] classification dirigée
[Termes IGN] densité d'information
[Termes IGN] données localisées de référence
[Termes IGN] jeu de données localisées
[Termes IGN] representativitéRésumé : (Auteur) Land cover maps obtained from classification of remotely sensed imagery provide valuable information in numerous environmental monitoring and modeling tasks. However, many uncertainties and errors can directly or indirectly affect the quality of derived maps. This work focuses on one key aspect of the supervised classification process of remotely sensed imagery: the quality of the reference dataset used to develop a classifier. More specifically, the representative power of the reference dataset is assessed by contrasting it with the full dataset (e.g. entire image) needing classification. Our method is applicable in several ways: training or testing datasets (extracted from the reference dataset) can be compared with the full dataset. The proposed method moves beyond spatial sampling schemes (e.g. grid, cluster) and operates in the multidimensional feature space (e.g. spectral bands) and uses spatial statistics to compare information density of data to be classified with data used in the reference process. The working hypothesis is that higher information density, not in general but with respect to the entire classified image, expresses higher confidence in obtained results. Presented experiments establish a close link between confidence metrics and classification accuracy for a variety of image classifiers namely maximum likelihood, decision tree, Backpropagation Neural Network and Support Vector Machine. A sensitivity analysis demonstrates that spatially-continuous reference datasets (e.g. a square window) have the potential to provide similar classification confidence as typically-used spatially-random datasets. This is an important finding considering the higher acquisition costs for randomly distributed datasets. Furthermore, the method produces confidence maps that allow spatially-explicit comparison of confidence metrics within a given image for identification of over- and under-represented image portions. The current method is presented for individual image classification but, with sufficient evaluation from the remote sensing community it has the potential to become a standard for reference dataset reporting and thus allowing users to assess representativeness of reference datasets in a consistent manner across different classification tasks. Numéro de notice : A2013-183 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.01.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.01.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32321
in ISPRS Journal of photogrammetry and remote sensing > vol 78 (April 2013) . - pp 129 - 147[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013041 RAB Revue Centre de documentation En réserve L003 Disponible Classification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)
[article]
Titre : Classification and reconstruction from random projections for hyperspectral imagery Type de document : Article/Communication Auteurs : W. Li, Auteur ; S. Prasad, Auteur ; J. Fowler, Auteur Année de publication : 2013 Article en page(s) : pp 833 - 843 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse en composantes principales
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'imageRésumé : (Auteur) There is increasing interest in dimensionality reduction through random projections due in part to the emerging paradigm of compressed sensing. It is anticipated that signal acquisition with random projections will decrease signal-sensing costs significantly; moreover, it has been demonstrated that both supervised and unsupervised statistical learning algorithms work reliably within randomly projected subspaces. Capitalizing on this latter development, several class-dependent strategies are proposed for the reconstruction of hyperspectral imagery from random projections. In this approach, each hyperspectral pixel is first classified into one of several pixel groups using either a conventional supervised classifier or an unsupervised clustering algorithm. After the grouping procedure, a suitable reconstruction method, such as compressive projection principal component analysis, is employed independently within each group. Experimental results confirm that such class-dependent reconstruction, which employs statistics pertinent to each class as opposed to the global statistics estimated over the entire data set, results in more accurate reconstructions of hyperspectral pixels from random projections. Numéro de notice : A2013-082 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2204759 En ligne : https://doi.org/10.1109/TGRS.2012.2204759 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32220
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 2 (February 2013) . - pp 833 - 843[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013021 RAB Revue Centre de documentation En réserve L003 Disponible Ground filtering and vegetation mapping using multi-return terrestrial laser scanning / Francesco Pirotti in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)
[article]
Titre : Ground filtering and vegetation mapping using multi-return terrestrial laser scanning Type de document : Article/Communication Auteurs : Francesco Pirotti, Auteur ; A. Guarnieri, Auteur ; Antonio Vettore, Auteur Année de publication : 2013 Article en page(s) : pp 56 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] carte de la végétation
[Termes IGN] classification dirigée
[Termes IGN] densité de la végétation
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] hauteur de la végétation
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Discriminating laser scanner data points belonging to ground from points above-ground (vegetation or buildings) is a key issue in research. Methods for filtering points into ground and non-ground classes have been widely studied mostly on datasets derived from airborne laser scanners, less so for terrestrial laser scanners. Recent developments in terrestrial laser sensors (longer ranges, faster acquisition and multiple return echoes) has aroused greater interest for surface modelling applications. The downside of TLS is that a typical dataset has high variability in point density, with evident side-effects on processing methods and CPU-time. In this work we use a scan dataset from a sensor which returns multiple target echoes, in this case providing more than 70 million points on our study site. The area presents low, medium and high vegetation, undergrowth with varying density, as well as bare ground with varying morphology (i.e. very steep slopes as well as flat areas). We test an integrated work-flow for defining a terrain and surface model (DTM and DSM) and successively for extracting information on vegetation density and height distribution on such a complex environment. Attention was given to efficiency and speed of processing. The method consists on a first step which subsets the original points to define ground candidates by taking into account the ordinal return number and the amplitude. A custom progressive morphological filter (opening operation) is applied next, on ground candidate points using a multidimensional grid to account for the fallout in point density as a function of distance from scanner. Vegetation density mapping over the area is then estimated using a weighted ratio of point counts in the tri-dimensional space over each cell. The overall result is a pipeline for processing TLS points clouds with minimal user interaction, producing a Digital Terrain Model (DTM), a Digital Surface Model (DSM), a vegetation density map and a derived Canopy Height Model (CHM). These products are of high importance for many applications ranging from forestry to hydrology and geomorphology. Numéro de notice : A2013-092 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.08.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.08.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32230
in ISPRS Journal of photogrammetry and remote sensing > vol 76 (February 2013) . - pp 56 - 63[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013021 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)
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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]Trajectories of moving objects on a network: detection of similarities, visualization of relations, and classification of trajectories / Yukio Sadahiro in Transactions in GIS, vol 17 n° 1 (February 2013)PermalinkPermalinkPermalinkDéveloppement d'outils et de méthodes pour l'estimation de la qualité des résultats de classification / Zhour Najoui (2013)PermalinkPermalinkOutils de modélisation SIG pour l'étude de la vulnérabilité côtière / Elmdari Souhail (2013)PermalinkSuper-resolution image analysis as a means of monitoring bracken (Pteridium aquilinum) distributions / Jennie Holland in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)PermalinkTree 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)PermalinkEvaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands, India, using IRS LISS III satellite images / E.P. Nobi in Geocarto international, vol 27 n° 8 (December 2012)PermalinkLa télédétection pour la cartographie de la trame verte en milieu agricole : Évaluation des potentialités d’images multi-angulaires à très haute résolution spatiale / David Sheeren in Revue internationale de géomatique, vol 22 n° 4 (décembre 2012 – février 2013)PermalinkMapping tropical forests and rubber plantations in complex landscapes by integrating PALSAR and MODIS imagery / J. Dong in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)PermalinkA supervised and fuzzy-based approach determine optimal multi-resolution image segmentation parameters / H. Tong in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 10 (October 2012)PermalinkInformation fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)PermalinkMapping crop types, irrigated areas, and cropping intensities in heterogeneous landscapes of southern India using multi-temporal medium-resolution imagery: implications for assessing water use in agriculture / E. Heller in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)PermalinkA comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region / Dong Lu ; E. Moran ; et al. in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)PermalinkComparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points / Y. Shao in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)PermalinkA framework for supervised image classification with incomplete training samples / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)PermalinkView generation for multiview maximum disagreement based active learning for hyperspectral image classification / W. Di in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)PermalinkEfficient parallel algorithm for pixel classification in remote sensing imagery / U. Maulik in Geoinformatica, vol 16 n° 2 (April 2012)PermalinkDétection et identification de zones de végétation arborée: utilisation conjointe d'images satellite RapidEye et de données BDOrtho / François Tassin (2012)Permalink