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Exploring the decision tree method for modelling urban land use change / Mileva Samardžić-Petrović in Geomatica, vol 69 n° 3 (september 2015)
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Titre : Exploring the decision tree method for modelling urban land use change Type de document : Article/Communication Auteurs : Mileva Samardžić-Petrović, Auteur ; Suzana Dragićević, Auteur ; Branislav Baja, Auteur ; Miloš Kovačević, Auteur Année de publication : 2015 Article en page(s) : pp 313 - 325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] aménagement du territoire
[Termes IGN] analyse diachronique
[Termes IGN] Belgrade
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par arbre de décision
[Termes IGN] impact sur l'environnement
[Termes IGN] milieu urbain
[Termes IGN] utilisation du solRésumé : (auteur) Les changements dans l'affectation des terres jouent un rôle important dans les interactions entre les humains et les systèmes physiques et ont des impacts importants sur l'environnement aux échelles locale, régionale et mondiale. Le changement d'affectation des terres est un processus complexe. C'est pourquoi le développement de modèles dynamiques pour représenter ce processus constitue une tâche remplie de défis. L'arbre de décision (AD) est une méthode d'apprentissage machine qui a la capacité d'extraire des tendances et de produire un modèle représentatif qui utilise les données géospatiales historiques. Même si l'arbre de décision est utilisé en télédétection comme méthode de classification, il n'est pas suffisamment examiné dans la science de l'aménagement du territoire. L'objectif principal du présent article de recherche est d'examiner la capacité de la méthode de l'arbre de décision pour modéliser le changement d'affectation des terres urbaines. Nous avons utilisé un nombre varié d'attributs pour trois municipalités dans la ville de Belgrade en République de Serbie. L'utilisation des terres est représentée à l'aide de neuf classes d'utilisation des terres sur trois différentes périodes de temps au cours des années 2003, 2007 et 2011. Les statistiques « kappa » et la courbe pondérée de la fonction d'efficacité du récepteur (CFER) ont été utilisées pour comparer les extrants du modèle à des jeux de données réelles d'utilisation des terres pour l'année 2011. Les valeurs maximales obtenues pour les statistiques kappa et la CFER indiquent que l'arbre de décision est une méthode utile pour modéliser le changement d'affectation des terres urbaines. En outre, l'arbre de classification qui en découle produit de l'information sur la relation entre les facteurs de causalité pris en compte et les changements d'affectation des terres et permet de mieux comprendre le processus de changement. Numéro de notice : A2015-668 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5623/cig2015-305 En ligne : https://doi.org/10.5623/cig2015-305 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78277
in Geomatica > vol 69 n° 3 (september 2015) . - pp 313 - 325[article]Measuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery / X. Chen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)
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Titre : Measuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery Type de document : Article/Communication Auteurs : X. Chen, Auteur ; Tao Fang, Auteur ; Hong Huo, Auteur ; Deren Li, Auteur Année de publication : 2015 Article en page(s) : pp 4837 - 4851 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données thématiques
[Termes IGN] extraction automatique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] image satelliteRésumé : (Auteur) Generally, some object-based features are more relevant to a thematic class than other features. These strongly relevant features, termed as class-specific features, would significantly contribute to thematic information extraction for very high resolution (VHR) images. However, many existing feature selection methods have been designed to select a good feature subset for all classes, rather than an independent feature subset for the thematic class. The latter might better meet the requirement of thematic information extraction than the former. In addition, the lack of quantitative evaluation of the contribution of the selected features to thematic classes also weakens our understandability of these features. To address the problems, class-specific feature selection methods are developed to measure the effectiveness of features for extracting thematic information from VHR images. First, the one-versus-all scheme is combined with traditional feature selection methods, such as ReliefF and LeastC. Also, one-versus-one scheme is utilized for alleviating the negative impact of a class imbalance problem arising from the one-versus-all scheme. Then, the relative contributions of features to thematic classes are obtained by the class-specific feature selection methods to describe the effectiveness of features for thematic information extraction. Finally, the class-specific feature selection methods are compared with the original methods on three different VHR image data sets by the nearest neighbor and support vector machine. Experimental results show that the class-specific feature selection methods outperform the corresponding conventional methods, and the one-versus-one scheme surpasses one-versus-all scheme. Additionally, many features are evaluated by the class-specific feature selection methods, to provide end users advice on effectiveness of the features. Numéro de notice : A2015-529 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2411331 Date de publication en ligne : 27/03/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2411331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77557
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4837 - 4851[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015091 SL Revue Centre de documentation Revues en salle Disponible On spectral unmixing resolution using extended support vector machines / Xiaofeng Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)
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Titre : On spectral unmixing resolution using extended support vector machines Type de document : Article/Communication Auteurs : Xiaofeng Li, Auteur ; Xiuping Jia, Auteur ; Liguo Wang, Auteur ; Kai Zhao, Auteur Année de publication : 2015 Article en page(s) : pp 4985 - 4996 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse infrapixellaire
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification pixellaire
[Termes IGN] classification spectraleRésumé : (Auteur) Due to the limited spatial resolution of multispectral/hyperspectral data, mixed pixels widely exist and various spectral unmixing techniques have been developed for information extraction at the subpixel level in recent years. One of the challenging problems in spectral mixture analysis is how to model the data of a primary class. Given that the within-class spectral variability (WSV) is inevitable, it is more realistic to associate a group of representative spectra with a pure class. The unmixing method using the extended support vector machines (eSVMs) has handled this problem effectively. However, it has simplified WSV in the mixed cases. In this paper, a further development of eSVMs is presented to address two problems in multiple-endmember spectral mixture analysis: 1) one mixed pixel may be unmixed into different fractions (model overlap); and 2) one fraction may correspond to a group of mixed pixels (fraction overlap). Then, spectral unmixing resolution (SUR) is introduced to characterize how finely the mixture in a mixed pixel can be quantified. The quantitative relationship between SUR and WSV of endmembers is derived via a geometry analysis in support vector machine feature space. Thus, the possible SUR can be estimated when multiple endmembers for each class are given. Moreover, if the requirement of SUR is fixed, the acceptance level of WSV is then limited, which can be used as a guide to remove outliers and purify endmembers for each primary class. Experiments are presented to illustrate model and fraction overlap problems and the application of SUR in uncertainty analysis of spectral unmixing. Numéro de notice : A2015-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2415587 Date de publication en ligne : 21/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2415587 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77555
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4985 - 4996[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015091 SL Revue Centre de documentation Revues en salle Disponible Region-kernel-based support vector machines for hyperspectral image classification / Jiangtao Peng in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)
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Titre : Region-kernel-based support vector machines for hyperspectral image classification Type de document : Article/Communication Auteurs : Jiangtao Peng, Auteur ; C.L. Philip Chen, Auteur ; Yicong Zhou, Auteur Année de publication : 2015 Article en page(s) : pp 4810 - 4824 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification barycentrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fonction régionalisée
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) This paper proposes a region kernel to measure the region-to-region distance similarity for hyperspectral image (HSI) classification. The region kernel is designed to be a linear combination of multiscale box kernels, which can handle the HSI regions with arbitrary shape and size. Integrating labeled pixels and labeled regions, we further propose a region-kernel-based support vector machine (RKSVM) classification framework. In RKSVM, three different composite kernels are constructed to describe the joint spatial-spectral similarity. Particularly, we design a desirable stack composite kernel that consists of the point-based kernel, the region-based kernel, and the cross point-to-region kernel. The effectiveness of the proposed RKSVM is validated on three benchmark hyperspectral data sets. Experimental results show the superiority of our region kernel method over the classical point kernel methods. Numéro de notice : A2015-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2410991 Date de publication en ligne : 06/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2410991 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77554
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 4810 - 4824[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015091 SL Revue Centre de documentation Revues en salle Disponible TerraSAR-X dual-pol time-series for mapping of wetland vegetation / Julie Betbeder in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)
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Titre : TerraSAR-X dual-pol time-series for mapping of wetland vegetation Type de document : Article/Communication Auteurs : Julie Betbeder, Auteur ; Sébastien Rapinel, Auteur ; Samuel Corgne, Auteur ; Eric Pottier, Auteur ; Laurence Hubert-Moy, Auteur Année de publication : 2015 Article en page(s) : pp 90 - 98 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] 1:10.000
[Termes IGN] caractérisation
[Termes IGN] carte de la végétation
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données multitemporelles
[Termes IGN] données polarimétriques
[Termes IGN] image radar moirée
[Termes IGN] image TerraSAR-X
[Termes IGN] série temporelle
[Termes IGN] zone humideRésumé : (auteur) Mapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using Support Vector Machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restoration) and biodiversity studies using remote sensing data. Numéro de notice : A2015-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78377
in ISPRS Journal of photogrammetry and remote sensing > vol 107 (September 2015) . - pp 90 - 98[article]An unsupervised urban change detection procedure by using luminance and saturation for multispectral remotely sensed images / Su Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)
PermalinkAutomatic identification of building types based on topographic databases – a comparison of different data sources / Robert Hecht in International journal of cartography, vol 1 n° 1 (August 2015)
PermalinkA fast classification scheme in Raman spectroscopy for the identification of mineral mixtures using a large database with correlated predictors / Corey J. Cochrane in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)
PermalinkNormalization of TanDEM-X DSM data in urban environments with morphological filters / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)
PermalinkSpectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)
PermalinkTesting the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods / Samuel Adelabu in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)
PermalinkDétection à haute résolution spatiale de la desserte forestière en milieu montagneux / António Ferraz in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)
PermalinkEstimation de la déforestation des forêts humides à Madagascar utilisant une classification multidate d'images Landsat entre 2005, 2010 et 2013 / F.A. Rakotomala in Revue Française de Photogrammétrie et de Télédétection, n° 211 - 212 (juillet - décembre 2015)
PermalinkGenetic differentiation of European larch along an altitudinal gradient in the French Alps / Maxime Nardin in Annals of Forest Science, vol 72 n° 5 (July 2015)
PermalinkLocal binary patterns and extreme learning machine for hyperspectral imagery classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
PermalinkOperationalizing measurement of forest degradation: Identification and quantification of charcoal production in tropical dry forests using very high resolution satellite imagery / K. Dons in International journal of applied Earth observation and geoinformation, vol 39 (July 2015)
PermalinkRandom Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features / Peijun Du in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
PermalinkSavannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
PermalinkSemantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers / Martin Weinmann in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
PermalinkSemisupervised transfer component analysis for domain adaptation in remote sensing image classification / Giona Matasci in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)
PermalinkCompilation de données radar et optiques pour la cartographie des classes d'occupation du sol aux environs du système lacustre de Bizerte (Tunisie du Nord) / Ibtissem Amri in Photo interprétation, European journal of applied remote sensing, vol 51 n° 2 (juin 2015)
PermalinkFast forward feature selection of hyperspectral images for classification with gaussian mixture models / Mathieu Fauvel in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 8 n° 6 (June 2015)
PermalinkA fully-automated approach to land cover mapping with airborne LiDAR and high resolution multispectral imagery in a forested suburban landscape / Jason R. Parent in ISPRS Journal of photogrammetry and remote sensing, vol 104 (June 2015)
PermalinkIntegrating 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)
PermalinkMulti-label class assignment in land-use modelling / Hichem Omrani in International journal of geographical information science IJGIS, vol 29 n° 6 (June 2015)
PermalinkSubstance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
PermalinkTerraMobilita/iQmulus urban point cloud analysis benchmark / Bruno Vallet in Computers and graphics, vol 49 (June 2015)
PermalinkAn evaluation and classification of nD topological data structures for the representation of objects in a higher-dimensional GIS / Ken Arroyo Ohori in International journal of geographical information science IJGIS, vol 29 n° 5 (May 2015)
PermalinkComplementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkForest species recognition based on dynamic classifier selection and dissimilarity feature vector representation / J.G. Martins in Machine Vision and Applications, vol 26 n° 2-3 (April 2015)
PermalinkHyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkIrregular variations in GPS time series by probability and noise analysis / Anna Klos in Survey review, vol 47 n° 342 (May 2015)
PermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkActive learning with gaussian process classifier for hyperspectral image classification / Shujing Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkAutomatic selection of landmarks for navigation guidance / Rui Zhu in Transactions in GIS, vol 19 n° 2 (April 2015)
PermalinkFast subpixel mapping algorithms for subpixel resolution change detection / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkLinear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkOn reverse-k-nearest-neighbor joins / Tobias Emrich in Geoinformatica, vol 19 n° 2 (April - June 2015)
PermalinkPanorama sur les méthodes de classification des images satellites et techniques d'amélioration de la précision de la classification / O. El Kharki in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)
PermalinkContextual classification of point cloud data by exploiting individual 3d neigbourhoods / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
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PermalinkExtracting mobile objects in images using a Velodyne lidar point cloud / Bruno Vallet in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
PermalinkAn adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkAn experimental approach for selection/elimination in stream network generalization using support vector machines / Alper Sen in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)
PermalinkAppariement hiérarchique de réseaux hydrographiques imparfaits / Benoit Costes in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)
PermalinkCharacterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm / Oumer S. Ahmed in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)
PermalinkCollaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkEmploying ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)
PermalinkSemisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization / Zhangyang Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
PermalinkVectorisation automatique des forêts dans les minutes de la carte d’état-major du 19e siècle / Pierre-Alexis Herrault in Revue internationale de géomatique, vol 25 n° 1 (mars - mai 2015)
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