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Employing 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)
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Titre : Employing ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland Type de document : Article/Communication Auteurs : Samuel Adelabu, Auteur ; Timothy Dube, Auteur Année de publication : 2015 Article en page(s) : pp 457 - 471 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse diachronique
[Termes IGN] Botswana
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données de terrain
[Termes IGN] espèce végétale
[Termes IGN] forêt
[Termes IGN] image hyperspectrale
[Termes IGN] image Quickbird
[Termes IGN] rééchantillonnage
[Termes IGN] réflectance végétale
[Termes IGN] savaneRésumé : (Auteur) With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n = 5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments. Numéro de notice : A2015-306 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.885589 Date de publication en ligne : 31/03/2014 En ligne : https://doi.org/10.1080/10106049.2014.885589 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76524
in Geocarto international > vol 30 n° 3 - 4 (March - April 2015) . - pp 457 - 471[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Improved area-based deformation analysis of a radio telescope’s main reflector based on terrestrial laser scanning / Christoph Holst in Journal of applied geodesy, vol 9 n° 1 (March 2015)
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Titre : Improved area-based deformation analysis of a radio telescope’s main reflector based on terrestrial laser scanning Type de document : Article/Communication Auteurs : Christoph Holst, Auteur ; Axel Nothnagel, Auteur ; Martin Blome, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1 - 14 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] auto-étalonnage
[Termes IGN] déformation d'édifice
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] radiotélescope
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) The main reflectors of radio telescopes deform due to gravitation when changing their elevation angle. This can be analyzed by scanning the paraboloid surface with a terrestrial laser scanner and by determining focal length variations and local deformations from best-fit approximations.
For the Effelsberg radiotelescope, both groups of deformations are estimated from seven points clouds measured at different elevation angles of the telescope: the focal length decreases by 22.7 mm when tilting the telescope from 90 deg to 7.5 deg elevation angle. Variable deformations of ± 2 mm are detected as well at certain areas. Furthermore, a few surface panels seem to be misaligned.
Apart from these results, the present study highlights the need for an appropriate measurement concept and for preprocessing stepswhen using laser scanners for area-based deformation analyses. Especially, data reduction, object segmentation and laser scanner calibration are discussed in more detail. An omission of these steps would significantly degrade the deformation analysis and the significance of its results. This holds for all sorts of laser scanner based analyses.Numéro de notice : A2015-026 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article DOI : 10.1515/jag-2014-0018 En ligne : http://www.degruyter.com/view/j/jag.2015.9.issue-1/jag-2014-0018/jag-2014-0018.x [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76879
in Journal of applied geodesy > vol 9 n° 1 (March 2015) . - pp 1 - 14[article]Polarimetric incoherent target decomposition by means of independent component analysis / Nikola Besic in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Polarimetric incoherent target decomposition by means of independent component analysis Type de document : Article/Communication Auteurs : Nikola Besic , Auteur ; Gabriel Vasile, Auteur ; Jocelyn Chanussot, Auteur ; et al., Auteur
Année de publication : 2015 Article en page(s) : pp 1236 - 1247 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] décomposition d'image
[Termes IGN] données polarimétriques
[Termes IGN] image radar
[Termes IGN] polarimétrie radarRésumé : (Auteur) This paper presents an alternative approach for polarimetric incoherent target decomposition (ICTD) dedicated to the analysis of very high-resolution polarimetric synthetic aperture radar (POLSAR) images. Given the non-Gaussian nature of the heterogeneous POLSAR clutter due to the increase in spatial resolution, the conventional methods based on the eigenvector target decomposition can ensure uncorrelation of the derived backscattering components at most. By introducing the independent component analysis (ICA) in lieu of the eigenvector decomposition, our method is rather deriving statistically independent components. The adopted algorithm, i.e., FastICA, uses the non-Gaussianity of the components as the criterion for their independence. Considering the eigenvector decomposition as being analogs to the principal component analysis (PCA), we propose the generalization of the ICTD methods to the level of the blind source separation (BSS) techniques (comprising both PCA and ICA). The proposed method preserves the invariance properties of the conventional ones, appearing to be robust both with respect to the rotation around the line of sight and to the change of the polarization basis. The efficiency of the method is demonstrated comparatively using POLSAR RAMSES X-band and ALOS L-band data sets. The main differences with respect to the conventional methods are mostly found in the behavior of the second most dominant component, which is not necessarily orthogonal to the first one. The potential of retrieving nonorthogonal mechanisms is moreover demonstrated using synthetic data. On the expense of a negligible entropy increase, the proposed method is capable of retrieving the edge diffraction of an elementary trihedral by recognizing dipole as the second component. Numéro de notice : A2015-137 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2336381 Date de publication en ligne : 28/07/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2336381 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75804
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1236 - 1247[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised 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)
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Titre : Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization Type de document : Article/Communication Auteurs : Zhangyang Wang, Auteur ; Nasser M. Nasrabadi, Auteur ; Thomas S. Huang, Auteur Année de publication : 2015 Article en page(s) : pp 1161 - 1173 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification pixellaire
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectraleRésumé : (Auteur) We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results. Numéro de notice : A2015-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2335177 Date de publication en ligne : 30/07/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2335177 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75792
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1161 - 1173[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Supervised 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)
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Titre : Supervised spectral–spatial hyperspectral image classification with weighted markov random fields Type de document : Article/Communication Auteurs : Le Sun, Auteur ; Zebin Wu, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1490 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] pondération
[Termes IGN] régression logistiqueRésumé : (Auteur) This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic. Numéro de notice : A2015-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2344442 Date de publication en ligne : 18/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2344442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75800
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1490 - 1503[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Vectorisation 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)
PermalinkDensity-based clustering for data containing two types of points / Tao Pei in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)
PermalinkGabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
PermalinkHyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
PermalinkJoint segmentation of multiple GPS coordinate series / Julien Gazeaux in Journal de la Société Française de Statistique, vol 156 n° 4 ([01/02/2015])
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PermalinkSparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
PermalinkStable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images / Julien Michel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
PermalinkUsing geographically weighted regression kriging for crop yield mapping in West Africa / Muhammad Imran in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)
PermalinkPermalinkAn abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkApplication à large échelle de techniques d'analyse d'images basées objet pour l'imagerie satellite à très haute résolution / David Youssefi in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)
PermalinkAutomatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkPermalinkConception d’une méthode de consolidation de grands réseaux lasergrammétriques / Emmanuel Clédat (2015)
PermalinkContribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (2015)
PermalinkData-driven feature learning for high resolution urban land-cover classification / Piotr Andrzej Tokarczyk (2015)
PermalinkDélimitation des parcelles agricoles par classification d'images Pléiades / Nesrine Chehata in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)
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PermalinkPermalinkEtude de l'évolution de l'utilisation du sol dans le district Sunsari (plaine du Népal) depuis les années 1950 / Mathilde Dumont-Aublin (2015)
PermalinkEvaluation de dégâts de tempête à l'échelle infra-parcellaire à partir d'une image Pléiades à très haute résolution sur un massif forestier feuillu en France / Anne Jolly in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)
PermalinkExtended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkA Feasibility study on occupants' behaviour and energy usage patterns and its potential integration with building information modelling / Liangxiu Han in International journal of 3-D information modeling, vol 4 n° 1 (January - March 2015)
PermalinkFusion of Lidar and SAR data for land-cover mapping in natural environments / Clara Barbanson (2015)
PermalinkPermalinkImproved land cover mapping using aerial photographs and satellite images / Katalin Varga in Open geosciences, vol 7 n° 1 (January 2015)
PermalinkLand cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests / Ning Lu in Geocarto international, vol 30 n° 1 - 2 (January - February 2015)
PermalinkMediterranean forest species mapping using classification of Hyperion imagery / Georgia Galidaki in Geocarto international, vol 30 n° 1 - 2 (January - February 2015)
PermalinkMODIS-based vegetation index has sufficient sensitivity to indicate stand-level intra-seasonal climatic stress in oak and beech forests / Tomáš Hlásny in Annals of Forest Science, vol 72 n° 1 (January 2015)
PermalinkPermalinkPermalinkOptimisation de la configuration d’un instrument superspectral aéroporté pour la classification : application au milieu urbain / Arnaud Le Bris (2015)
PermalinkA Random Forest class memberships based wrapper band selection criterion : application to hyperspectral / Arnaud Le Bris (2015)
PermalinkSpatial-aware dictionary learning for hyperspectral image classification / Ali Soltani-Farani in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkSpectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
PermalinkPermalinkTropical forest structure characterization using airborne lidar data: an individual tree level approach / António Ferraz (dec 2015)
PermalinkUse of remotely sensed auxiliary data for improving sample-based forest inventories / Svetlana Saarela (2015)
PermalinkDomain adaptation for land use classification: A spatio-temporal knowledge reusing method / Yilun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)
PermalinkEtude de l'usage de la couleur dans l'apprentissage des SIG en géosciences : le cas de la cartographie d'aptitude / Raffaella Balzarini in Cartes & Géomatique, n° 222 (décembre 2014)
PermalinkManifold-based sparse representation for hyperspectral image classification / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)
PermalinkSegmentation semi-automatique pour le traitement de données 3D denses : application au patrimoine architectural / Florent Poux in XYZ, n° 141 (décembre 2014 - février 2015)
PermalinkSpectral–spatial hyperspectral image classification via multiscale adaptive sparse representation / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 12 (December 2014)
PermalinkAccuracy test of point-based and object-based urban building feature classification and extraction applying airborne LiDAR data / T. Tang in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)
PermalinkAssociation-matrix-based sample consensus approach for automated registration of terrestrial laser scans using linear features / Kaleel Al-Durgham in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)
PermalinkA new sparse source separation-based classification approach / M.A. Loghmari in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)
PermalinkPer-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery / Mustafa Neamah Jebur in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)
PermalinkA robust image matching method based on optimized BaySAC / Zhizhong Kang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 11 (November 2014)
PermalinkSemi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation / L. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)
PermalinkApport de l'imagerie très haute résolution spatiale pour la caractérisation de la densité urbaine / Laurent Bouffier in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)
PermalinkMathematical morphology pre-processing for enhanced segmentation of heterogeneous spatial regions / Julien Radoux in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)
PermalinkObject-based hyperspectral classification of urban areas using marker-based hierarchical segmentation / Davood Akbari in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)
PermalinkQuantification et cartographie de la structure forestière à partir de la texture des images Pléiades / Benoit Beguet in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)
PermalinkSubspace-based technique for speckle noise reduction in SAR images / Norashikin Yahya in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)
PermalinkToward satellite-based land cover classification through optimum-path forest / Rodrigo José Pisani in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)
PermalinkSemantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 (September 2014)
PermalinkPermalinkCharacterisation of building alignments with new measures using C4.5 decision tree algorithm / Sinan Cetinkaya in Geodetski vestnik, vol 58 n° 3 ([01/09/2014])
PermalinkClassification of submerged aquatic vegetation in Black River using hyperspectral image analysis / Roshan Pande-Chhetri in Geomatica, vol 68 n° 3 (September 2014)
PermalinkDetection of systematic displacements in spatial databases using linear elements / A. Mozas-Calvache in Cartography and Geographic Information Science, vol 41 n° 4 (September 2014)
PermalinkIGS-MGEX, on prépare le terrain pour les sciences et techniques GNSS multi-constellation / Oliver Montenbruck in XYZ, n° 140 (septembre - novembre 2014)
PermalinkLand cover and soil type mapping from spaceborne PolSAR Data at L-Band with probabilistic neural network / Oleg Antropov in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)
PermalinkSpectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)
PermalinkA user study of experimental maps for outdoor activities / Juha Oksanen in Cartographica, vol 49 n° 3 (September 2014)
PermalinkAn intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata based algorithm / P. V. Arun in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
PermalinkA class of cloud detection algorithms based on a MAP-MRF approach in space and time / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
PermalinkEstimation of the timber quality of scots pine with terrestrial laser scanning / Ville Kankare in Forests, vol 5 n° 8 (August 2014)
PermalinkGeospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products / Joseph P. Spruce in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
PermalinkHyperspectral data dimensionality reduction and the impact of multi-seasonal Hyperion EO-1 imagery on classification accuracies of tropical forest species / Manjit Saini in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
PermalinkHyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
PermalinkKernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
PermalinkRoad hierarchy with integration of attributes using fuzzy-AHP / Fatih Gülgen in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
PermalinkA rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images / Zahra Ziaei in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
PermalinkApproche de détermination de signature de texture : application à la classification de couverts forestiers d’image satellitaire à haute résolution / Wala Zaaboub in Revue Française de Photogrammétrie et de Télédétection, n° 207 (Juillet 2014)
PermalinkLand cover classification of finer resolution remote sensing data integrating temporal features from time series coarser resolution data / Kun Jia in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)
PermalinkNovel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing / Jaime Zabalza in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)
PermalinkPermalinkAn effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)
PermalinkUne approche basée objet combinée avec les classifieurs avancés (SVM, RF, Extra Trees) pour la détection des changements du bâti / Loubna Elmansouri in Revue internationale de géomatique, vol 24 n° 2 (juin - août 2014)
PermalinkCrop type classification by simultaneous use of satellite images of different resolutions / Mark W. Liu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkDecision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkDevelopment of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery / Alireza Hamedianfar in Geocarto international, vol 29 n° 3 - 4 (June - July 2014)
PermalinkDiscrimination des unités géologiques et structurales du socle précambrien de l'Afrique de l'ouest à l'aide de transformations multispectrales : cas du degré carré de Korhogo au nord de la Côte d'Ivoire / K. Kouamé in Photo interprétation, European journal of applied remote sensing, vol 50 n° 2 (juin 2014)
PermalinkLes effets de l'oscillation Nord-Atlantique sur les transferts de masse, vus par géodésie / Pierre Valty in XYZ, n° 139 (juin - août 2014)
PermalinkFeature extraction of hyperspectral images with image fusion and recursive filtering / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkMapping large spatial flow data with hierarchical clustering / Xi Zhu in Transactions in GIS, vol 18 n° 3 (June 2014)
PermalinkPerformance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery / Iman Khosravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)
PermalinkSemisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data / Shuyuan Yang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkSignificance analysis of different types of ancillary geodata utilized in a multisource classification process for forest identification in Germany / Michael Förster in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
PermalinkActive learning in the spatial domain for remote sensing image classification / André Stumpf in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)
PermalinkAmadeus : analyse de données massives en sciences de la Terre et de l'univers / Collectif Amadeus in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 19 n° 3 (mai - juin 2014)
PermalinkBig data : mise en perspective et enjeux pour les entreprises / Myriam Karoui in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 19 n° 3 (mai - juin 2014)
PermalinkBayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)
PermalinkA general framework for trajectory data warehousing and visual OLAP / Luca Leonardi in Geoinformatica, vol 18 n° 2 (April 2014)
PermalinkAutomatic registration of coastal remotely sensed imagery by affine invariant feature matching with shoreline constraint / Liang Cheng in Marine geodesy, vol 37 n° 1 (March - May 2014)
PermalinkEfficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding / Junwei Han in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)
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