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A 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)
[article]
Titre : A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; E. Moran, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 26 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse comparative
[Termes IGN] bande C
[Termes IGN] bande L
[Termes IGN] classification dirigée
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar
[Termes IGN] image Radarsat
[Termes IGN] occupation du sol
[Termes IGN] zone tropicale humideRésumé : (Auteur) This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. Numéro de notice : A2012-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31733
in ISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 26 - 38[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2012041 SL Revue Centre de documentation Revues en salle Disponible Comparison 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)
[article]
Titre : Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points Type de document : Article/Communication Auteurs : Y. Shao, Auteur ; R. Lunetta, Auteur Année de publication : 2012 Article en page(s) : pp 78 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse diachronique
[Termes IGN] classification dirigée
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Terra-MODIS
[Termes IGN] occupation du sol
[Termes IGN] Perceptron multicouche
[Termes IGN] série temporelleRésumé : (Auteur) Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two conventional nonparametric image classification algorithms: multilayer perceptron neural networks (NN) and classification and regression trees (CART). For 2001 MODIS time-series data, SVM generated overall accuracies ranging from 77% to 80% for training sample sizes from 20 to 800 pixels per class, compared to 67–76% and 62–73% for NN and CART, respectively. These results indicated that SVM’s had superior generalization capability, particularly with respect to small training sample sizes. There was also less variability of SVM performance when classification trials were repeated using different training sets. Additionally, classification accuracies were directly related to sample homogeneity/heterogeneity. The overall accuracies for the SVM algorithm were 91% (Kappa = 0.77) and 64% (Kappa = 0.34) for homogeneous and heterogeneous pixels, respectively. The inclusion of heterogeneous pixels in the training sample did not increase overall accuracies. Also, the SVM performance was examined for the classification of multiple year MODIS time-series data at annual intervals. Finally, using only the SVM output values, a method was developed to directly classify pixel purity. Approximately 65% of pixels within the Albemarle–Pamlico Basin study area were labeled as “functionally homogeneous” with an overall classification accuracy of 91% (Kappa = 0.79). The results indicated a high potential for regional scale operational land-cover characterization applications. Numéro de notice : A2012-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.04.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31736
in ISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 78 - 87[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2012041 SL Revue Centre de documentation Revues en salle Disponible Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions / M. Cutler in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)
[article]
Titre : Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions Type de document : Article/Communication Auteurs : M. Cutler, Auteur ; D. Boyd, Auteur ; Giles M. Foody, Auteur ; A. Vetrivel, Auteur Année de publication : 2012 Article en page(s) : pp 66 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] analyse texturale
[Termes IGN] biomasse
[Termes IGN] biomasse (combustible)
[Termes IGN] Brésil
[Termes IGN] classification par réseau neuronal
[Termes IGN] déboisement
[Termes IGN] forêt tropicale
[Termes IGN] image JERS
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image radar
[Termes IGN] Malaisie
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] ondelette
[Termes IGN] texture d'image
[Termes IGN] ThaïlandeRésumé : (Auteur) Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). Numéro de notice : A2012-289 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31735
in ISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 66 - 77[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2012041 SL Revue Centre de documentation Revues en salle Disponible A framework for automatic and unsupervised detection of multiple changes in multitemporal images / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)
[article]
Titre : A framework for automatic and unsupervised detection of multiple changes in multitemporal images Type de document : Article/Communication Auteurs : Francesca Bovolo, Auteur ; S. Marchesi, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2012 Article en page(s) : pp 2196 - 2212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] bande B
[Termes IGN] classification bayesienne
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] seuillage d'imageRésumé : (Auteur) The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images having B spectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach. Numéro de notice : A2012-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2171493 Date de publication en ligne : 21/11/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2171493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31710
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 6 (June 2012) . - pp 2196 - 2212[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012061 RAB Revue Centre de documentation En réserve L003 Disponible A framework for supervised image classification with incomplete training samples / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)
[article]
Titre : A framework for supervised image classification with incomplete training samples Type de document : Article/Communication Auteurs : Q. Guo, Auteur ; W. Li, Auteur ; J. Chen, Auteur Année de publication : 2012 Article en page(s) : pp 595 - 604 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] échantillonnage d'image
[Termes IGN] extraction de coucheRésumé : (Auteur) For traditional supervised classification methods, all land-cover types need to be exhaustively labeled to train the classifier. However, there are situations where the training sample classes are incomplete due to a lack of understanding of ground cover types in the image. In this study we propose a one-by-one (OBO) classification framework to address this incomplete training sample problem. The OBO approach is based on a one-class classifier (positive and unlabeled learning algorithm), and it extracts the land-cover type from the image one at a time. The performance of the proposed method was compared with a traditional supervised classifier using a high spatial resolution image. The average accuracy of the new method is 76.34 percent across different training sample sizes, whereas the accuracy of the classical approach is 66.46 percent, with an increase of 9.88 percent. The results demonstrate that the proposed new framework provides significantly higher classification accuracy than the classical approach at the 95 percent confidence level, and shows promise in dealing with the incomplete training sample problem for supervised image classification. Numéro de notice : A2012-249 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.6.595 En ligne : https://doi.org/10.14358/PERS.78.6.595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31695
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Händler in ZFV, Zeitschrift für Geodäsie, Geoinformation und Landmanagement, vol 137 n° 2 (01/03/2012)PermalinkHyperspectral unmixing based on mixtures of Dirichlet components / J. Nascimento in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)PermalinkMathematical morphology-based generalization of complex 3D building models incorporating semantic relationships / J. Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 68 (March 2012)PermalinkUtilisation d'images à haute et très haute résolution pour la mise à jour de la carte de l'Aquila (Italie) / Valerio Baiocchi in Géomatique expert, n° 85 (01/03/2012)PermalinkAutomated detection of prehistorical rock art features aided by TLS and 2D data co-registration / Jean-Baptiste Lamontre (2012)PermalinkDétection et identification de zones de végétation arborée: utilisation conjointe d'images satellite RapidEye et de données BDOrtho / François Tassin (2012)PermalinkA genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery / Dimitris G. Stavrakoudis in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)PermalinkModelling the Zn emissions from roofing materials at Créteil city scale : Defining a methodology / Emna Sellami-Kaaniche (2012)PermalinkPermalinkTraitements numériques des images de télédétection, Vol. 2. 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McRoberts in Remote sensing of environment, vol 115 n° 12 (december 2011)PermalinkRecognizing basic structures from mobile laser scanning data for road inventory studies / Shi Pu in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)PermalinkRelevance assessment of full-waveform lidar data for urban area classification / Clément Mallet in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)PermalinkCartographie des sols hydromorphes de la région des lacs (Côte d'Ivoire) par l'approche du spectral angle mapper (SAM) / G. Zro Bi in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)PermalinkClassification orientée-objet supervisée d'une forêt avec une sélection guidée d'attributs personnalisés / Olivier de Joinville in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)PermalinkImproving the Wishart synthetic aperture radar image classifications through deterministic simulated annealing / F. Sanchez-Llado in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 (November 2011)PermalinkPixel unmixing in hyperspectral data by means of neural networks / Giorgio Licciardi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)PermalinkSVM-based unmixing-to-classification conversion for hyperspectral abundance quantification / F. Mianji in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)PermalinkDamage assessment of 2010 Haïti earthquake with post-earthquake satellite image by support vector selection and adaptation / Gülsen Taskin Kaya in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 10 (October 2011)PermalinkDevelopment of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data / S. Khorram in Geocarto international, vol 26 n° 6 (October 2011)PermalinkOndelettes et théorie des évidences pour la classification orientée-objet : Caractérisation et suivi des changements d’occupation des sols de la métropole de Rennes / A. Lefebvre in Revue internationale de géomatique, vol 21 n° 3 (septembre - novembre 2011)PermalinkPostGIS pour les néophytes (6ème partie) : Le langage PL/PgSQL / Anonyme in Géomatique expert, n° 82 (01/09/2011)PermalinkPrediction of the error induced by topography in satellite microwave radiometric observations / Luca Pulvirenti in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)PermalinkSeismic-zonation of Port-au-Prince using pixel- object-based imaging analysis methods on Aster GDEM / S. Yong in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 9 (September 2011)PermalinkSimultaneous denoising and intrinsic order selection in hyperspectral imaging / M. Farzam in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)PermalinkPermalinkAutomatic classification of retail spaces from a large scale topographic database / William A Mackaness in Transactions in GIS, vol 15 n° 3 (July 2011)PermalinkAutomatic interpretation of digital maps / Volker Walter in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)PermalinkA comparison of fuzzy AHP and ideal point methods for evaluating land suitability / M. Elaalem in Transactions in GIS, vol 15 n° 3 (July 2011)PermalinkGiving the ‘right’ route directions : the requirements for pedestrian navigation systems / C. Schroder in Transactions in GIS, vol 15 n° 3 (July 2011)PermalinkApproche non supervisée par processus ponctuels marqués pour l'extraction d'objets à partir d'images aériennes et satellitaires / S. Ben Hadj in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)PermalinkDétection de bateaux dans les images satellitaires optiques panchromatiques / N. Proia in Revue Française de Photogrammétrie et de Télédétection, n° 194 (Mai 2011)PermalinkElectromagnetic land surface classification through integration of optical and radar remote sensing data / J. Baek in IEEE Transactions on geoscience and remote sensing, vol 49 n° 4 (April 2011)PermalinkHistorical land use as a feature for image classification / Jorge Abel Recio in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 4 (April 2011)PermalinkFull waveform-based analysis for forest type information derivation from large footprint spaceborne lidar data / Junjie Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 3 (March 2011)PermalinkLa télédétection et son application à l'étude de la végétation : quelques principes / Anne Jolly in Rendez-vous techniques, n° 31 (hiver 2011)PermalinkImpervious surface area extraction from IKONOS imagery using an object-based fuzzy method / Xuefei Hu in Geocarto international, vol 26 n° 1 (February 2011)PermalinkChange detection in a topographic building database using submetric satellite images / Arnaud Le Bris (2011)PermalinkDelineation of impervious surface from multispectral imagery and lidar incorporating knowledge based expert system rules / K. Germaine in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)PermalinkDétection de changement 2D à partir d’imagerie satellitaire : Application à la mise à jour des bases de données géographiques / Nicolas Champion (2011)PermalinkDonnées géographiques / Pierre Dumolard (2011)PermalinkGraph-based feature selection for object-oriented classification in VHR airborne imagery / Tianen Chen in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)PermalinkA hybrid classification scheme for mining multisource geospatial data / R. Vatsavai in Geoinformatica, vol 15 n° 1 (January 2011)PermalinkLand cover classification of cloud-contaminated multitemporal high-resolution images / A. Salberg in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)PermalinkParameterizing support vector machines for land cover classification / X. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)PermalinkPermalinkRelevance of airborne lidar and multispectral image data for urban scene classification using random forests / Li Guo in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 1 (January - February 2011)PermalinkGéographie de la mortalité routière en Europe / V. Eksler in Revue internationale de géomatique, vol 20 n° 4 (décembre 2010 – février 2011)PermalinkUse of high-resolution satellite imagery in an integrated model to predict the distribution of shade coffee tree hybrid zones / C. Gomez in Remote sensing of environment, vol 114 n° 11 (15/11/2010)PermalinkDTM generalisation : handling large volumes of data for multi-scale mapping / O. Chaudry in Cartographic journal (the), vol 47 n° 4 (November 2010)PermalinkLocal manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)PermalinkMultiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)PermalinkNoise-signal index threshold: a new noise-reduction technique for generation of reference spectra and efficient hyperspectral image classification / K. Kusuma in Geocarto international, vol 25 n° 7 (November 2010)PermalinkEffect of SRTM resolution on morphometric feature identification using neural network - self organizing map / A. Ehsani in Geoinformatica, vol 14 n° 4 (October 2010)PermalinkSimilarity weighted instance-based learning for the generation of transition potentials in land use change modeling / F. Sangermano in Transactions in GIS, vol 14 n° 5 (October 2010)PermalinkUncertainty analysis for the classification of multispectral satellite images using SVMs and SOMs / F. Giacco in IEEE Transactions on geoscience and remote sensing, vol 48 n° 10 (October 2010)PermalinkAutomatic fuzzy clustering using modified differential evolution for image classification / U. Maulik in IEEE Transactions on geoscience and remote sensing, vol 48 n° 9 (September 2010)PermalinkBackscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas / C. Alexander in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 5 (September - October 2010)PermalinkExtraction automatique des discontinuités planes à partir d'une scannérisation laser 3D en milieu rocheux / Souhail Hajri in Revue Française de Photogrammétrie et de Télédétection, n° 192 (Septembre 2010)PermalinkLes débuts de la télédétection spatiale dans la géographie française : témoignage d’un pionnier / Fernand Verger in L'information géographique, vol 74 n° 2 (août 2010)PermalinkSemisupervised one-class support vector machine for classification of remote sensing data / Jordi Munoz-Mari in IEEE Transactions on geoscience and remote sensing, vol 48 n° 8 (August 2010)PermalinkTerrestrial laser scanning and exploratory spatial data analysis for the mapping of weathering forms on rock art panels / B. Vogt in Geocarto international, vol 25 n° 5 (August 2010)Permalinkvol 15 n° 4 - juillet - août 2010 - Ingénierie d'entreprise et des systèmes d'information (Bulletin de Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI) / S. NurcanPermalinkApplication de la classification floue (fuzzy k-NN) à l'étude de l'occupation du sol d'une zone urbaine : le cas de la région de Genève / S. Rakotoniaina in Photo interprétation, European journal of applied remote sensing, vol 46 n° 2 (juin 2010)PermalinkL'apport de l'analyse d'images à la recherche des structures socio-spatiales : application au tissu bâti beyrouthin (1956-1999) / R. Zaarour in Le monde des cartes, n° 204 (juin 2010)PermalinkEffects of topographic variability and Lidar sampling density on several DEM interpolation methods / Q. Guo in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 6 (June 2010)PermalinkOn the integration of regional classification and delineation systems into The National Map / T. Bittner in Cartographica, vol 45 n° 2 (June 2010)PermalinkRobust Kalman filtering with constraints: a case study for integrated navigation / Y. Yang in Journal of geodesy, vol 84 n° 6 (June 2010)PermalinkSegmentation et interprétation de nuages de points pour la modélisation d'environnements urbains / J. Hernandez in Revue Française de Photogrammétrie et de Télédétection, n° 191 (Mai 2010)PermalinkAlgorithms for constrained k-nearest neighbor queries over moving object trajectories / Yunjun Gao in Geoinformatica, vol 14 n° 2 (April 2010)PermalinkDetection of roadway sign condition changes using multi-scale sign image matching (M-SIM) / Y.J. Tsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 4 (April 2010)PermalinkEfficient evaluation of continuous spatio-temporal queries on moving objects whith uncertain velocity / Y. Huang in Geoinformatica, vol 14 n° 2 (April 2010)PermalinkSome considerations on significance analysis for deformation detection via frequentist and Bayesian tests / F. Sacerdote in Journal of geodesy, vol 84 n° 4 (April 2010)PermalinkCommentaire de la carte des changements de l'occupation du sol dans les Rivières-du-Sud / J. Andrieu in Le monde des cartes, n° 203 (mars 2010)PermalinkConsistency of accuracy assessment indices for soft classification: simulation analysis / J. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 2 (March - April 2010)PermalinkExploration et représentation d'une matrice de flux / Marie Piron in Le monde des cartes, n° 203 (mars 2010)PermalinkUn graphe génératif pour la classification semi-supervisée / P. Gaillard in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 15 n° 2 (mars - avril 2010)PermalinkIndexation rapide de documents audio par traitement morphologique de la parole / F. Salama in Ingénierie des systèmes d'information, ISI : Revue des sciences et technologies de l'information, RSTI, vol 15 n° 2 (mars - avril 2010)PermalinkLand-cover change detection using one-class support vector machine / P. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 3 (March 2010)PermalinkTerrain modeling from Lidar range data in natural landscapes: a predictive and Bayesian framework / Frédéric Bretar in IEEE Transactions on geoscience and remote sensing, vol 48 n° 3 Tome 2 (March 2010)Permalink