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Semantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)
[article]
Titre : Semantic segmentation of road furniture in mobile laser scanning data Type de document : Article/Communication Auteurs : Fashuai Li, Auteur ; Matti Lehtomäki, Auteur ; Sander J. Oude Elberink, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 98 - 113 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] mobilier urbain
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) Road furniture recognition has become a prevalent issue in the past few years because of its great importance in smart cities and autonomous driving. Previous research has especially focussed on pole-like road furniture, such as traffic signs and lamp posts. Published methods have mainly classified road furniture as individual objects. However, most road furniture consists of a combination of classes, such as a traffic sign mounted on a street light pole. To tackle this problem, we propose a framework to interpret road furniture at a more detailed level. Instead of being interpreted as single objects, mobile laser scanning data of road furniture is decomposed in elements individually labelled as poles, and objects attached to them, such as, street lights, traffic signs and traffic lights. In our framework, we first detect road furniture from unorganised mobile laser scanning point clouds. Then detected road furniture is decomposed into poles and attachments (e.g. traffic signs). In the interpretation stage, we extract a set of features to classify the attachments by utilising a knowledge-driven method and four representative types of machine learning classifiers, which are random forest, support vector machine, Gaussian mixture model and naïve Bayes, to explore the optimal method. The designed features are the unary features of attachments and the spatial relations between poles and their attachments. Two experimental test sites in Enschede dataset and Saunalahti dataset were applied, and Saunalahti dataset was collected in two different epochs. In the experimental results, the random forest classifier outperforms the other methods, and the overall accuracy acquired is higher than 80% in Enschede test site and higher than 90% in both Saunalahti epochs. The designed features play an important role in the interpretation of road furniture. The results of two epochs in the same area prove the high reliability of our framework and demonstrate that our method achieves good transferability with an accuracy over 90% through employing the training data of one epoch to test the data in another epoch. Numéro de notice : A2019-266 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.001 Date de publication en ligne : 08/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93081
in ISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 98 - 113[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A cognitive framework for road detection from high-resolution satellite images / Naveen Chandra in Geocarto international, vol 34 n° 8 ([15/06/2019])
[article]
Titre : A cognitive framework for road detection from high-resolution satellite images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Jayanta Kumar Ghosh, Auteur ; Ashu Sharma, Auteur Année de publication : 2019 Article en page(s) : pp 909 - 924 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] cadre conceptuel
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] image satellite
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] représentation cognitive
[Termes IGN] zone urbaineRésumé : (auteur) Road network extraction from high-resolution satellite (HRS) imagery is a complex task. It is an important field of research and is widely used in various cartographic applications such as updating and generating maps. The objective of this research work is to develop a novel framework, emulating human cognition, for detection of roads from HRS images. Roads network from HRS images are detected using support vector machines within the different stages of cognitive task analysis. In the first stage, basic information about the cognitive parameters which are required for image interpretation is collected. In the second stage, the rule-based method is used for knowledge representation. Lastly, during knowledge elicitation, the developed rules are used to extract roads from HRS images. The proposed method is validated using 16 HRS images of developed suburban, developed urban, emerging suburban and emerging urban region. Numéro de notice : A2019-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1450451 Date de publication en ligne : 29/03/2018 En ligne : https://doi.org/10.1080/10106049.2018.1450451 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93869
in Geocarto international > vol 34 n° 8 [15/06/2019] . - pp 909 - 924[article]Mise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo / Cédric Lardeux in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)
[article]
Titre : Mise en oeuvre d'outils open source pour le suivi opérationnel de l'occupation des sols et de la déforestation à partir des données Sentinel radar optique : études de cas en Guyane et au Togo Type de document : Article/Communication Auteurs : Cédric Lardeux, Auteur ; Anoumou Kemavo, Auteur ; Maxence Rageade, Auteur ; Mathieu Rahm, Auteur ; Pierre-Louis Frison , Auteur ; Jean-Paul Rudant , Auteur Année de publication : 2019 Article en page(s) : pp 59 - 70 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] déboisement
[Termes IGN] Guyane (département français)
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] Orfeo Tool Box
[Termes IGN] Python (langage de programmation)
[Termes IGN] QGIS
[Termes IGN] série temporelle
[Termes IGN] surveillance de la végétation
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] TogoRésumé : (auteur) Remote sensing is a particularly suitable tool for monitoring land use but also particularly suited for deforestation monito-ring. By launching the Setinel-1 and Sentinel-2 satellites in the Copernicus program, the community now has free data with important time-revisits allowing the greatest number of people to effectively monitor land cover of a study area. This paper presents a land use monitoring method based on the combination of two approaches computed using Open Source tools ((MIS, Orfeo ToolBox, python). First, we focus on land use monitoring at an annual time scale using Sentinel-1 and Sentinel-2 data, and then, by the use f entienl-1 data, we detect changes in the forest cover due to deforestation at bi-monthly time scale. The results obtained show a very good synergy of these approaches allowing the complementary of optical and radar data. In order to make accessible the proposed method, all the used open source tools are available on this link http ://remotesensing4all.net/index.php/2018/09/11/ kit-use-des-donnees-radar- sentinel-1-in-de-latelier-radar-du-FOSS4G-en-2018-2/. Numéro de notice : A2019-348 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.52638/rfpt.2019.467 En ligne : https://doi.org/10.52638/rfpt.2019.467 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93385
in Revue Française de Photogrammétrie et de Télédétection > n° 219-220 (juin - octobre 2019) . - pp 59 - 70[article]Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery / Christian Geiss in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)
[article]
Titre : Virtual Support Vector Machines with self-learning strategy for classification of multispectral remote sensing imagery Type de document : Article/Communication Auteurs : Christian Geiss, Auteur ; Patrick Aravena Pelizari, Auteur ; Lukas Blickensdörfer, Auteur ; Hannes Taubenböck, Auteur Année de publication : 2019 Article en page(s) : pp 42 - 58 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Cologne
[Termes IGN] échantillon
[Termes IGN] échantillonnage
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] invariant
[Termes IGN] Kenya
[Termes IGN] séparateur à vaste margeRésumé : (Auteur) We follow the idea of learning invariant decision functions for remote sensing image classification with Support Vector Machines (SVM). To do so, we generate artificially transformed samples (i.e., virtual samples) from available prior knowledge. Labeled samples closest to the separating hyperplane with maximum margin (i.e., the Support Vectors) are identified by learning an initial SVM model. The Support Vectors are used for generating virtual samples by perturbing the features to which the model should be invariant. Subsequently, the model is relearned using the Support Vectors and the virtual samples to eventually alter the hyperplane with maximum margin and enhance generalization capabilities of decision functions. In contrast to existing approaches, we establish a self-learning procedure to ultimately prune non-informative virtual samples from a possibly arbitrary invariance generation process to allow for robust and sparse model solutions. The self-learning strategy jointly considers a similarity and margin sampling constraint. In addition, we innovatively explore the invariance generation process in the context of an object-based image analysis framework. Image elements (i.e., pixels) are aggregated to image objects (as represented by segments/superpixels) with a segmentation algorithm. From an initial singular segmentation level, invariances are encoded by varying hyperparameters of the segmentation algorithm in terms of scale and shape. Experimental results are obtained from two very high spatial resolution multispectral data sets acquired over the city of Cologne, Germany, and the Hagadera Refugee Camp, Kenya. Comparative model accuracy evaluations underline the favorable performance properties of the proposed methods especially in settings with very few labeled samples. Numéro de notice : A2019-203 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.03.001 Date de publication en ligne : 12/03/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.03.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92666
in ISPRS Journal of photogrammetry and remote sensing > vol 151 (May 2019) . - pp 42 - 58[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans / Tanumi Kumar in Geocarto international, vol 34 n° 4 ([15/03/2019])
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Titre : Discrimination and classification of mangrove forests using EO-1 Hyperion data : a case study of Indian Sundarbans Type de document : Article/Communication Auteurs : Tanumi Kumar, Auteur ; Abhishek Mandal, Auteur ; Dibyendu Dutta, Auteur ; R. Nagaraja, Auteur ; Vinay Kumar Dadhwal, Auteur Année de publication : 2019 Article en page(s) : pp 415 - 442 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse discriminante
[Termes IGN] classification barycentrique
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification Spectral angle mapper
[Termes IGN] image EO1-Hyperion
[Termes IGN] Inde
[Termes IGN] indice de végétation
[Termes IGN] mangrove
[Termes IGN] palétuvierRésumé : (Auteur) In remote sensing the identification accuracy of mangroves is greatly influenced by terrestrial vegetation. This paper deals with the use of specific vegetation indices for extracting mangrove forests using Earth Observing-1 Hyperion image over a portion of Indian Sundarbans, followed by classification of mangroves into floristic composition classes. Five vegetation indices (three new and two published), namely Mangrove Probability Vegetation Index, Normalized Difference Wetland Vegetation Index, Shortwave Infrared Absorption Index, Normalized Difference Infrared Index and Atmospherically Corrected Vegetation Index were used in decision tree algorithm to develop the mangrove mask. Then, three full-pixel classifiers, namely Minimum Distance, Spectral Angle Mapper and Support Vector Machine (SVM) were evaluated on the data within the mask. SVM performed better than the other two classifiers with an overall precision of 99.08%. The methodology presented here may be applied in different mangrove areas for producing community zonation maps at finer levels. Numéro de notice : A2019-451 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1408699 Date de publication en ligne : 11/12/2017 En ligne : https://doi.org/10.1080/10106049.2017.1408699 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92839
in Geocarto international > vol 34 n° 4 [15/03/2019] . - pp 415 - 442[article]Land cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms / Dimitri Bulatov in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkAilanthus altissima mapping from multi-temporal very high resolution satellite images / Cristina Tarantino in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkEnhancing the predictability of least-squares collocation through the integration with least-squares-support vector machine / Hossam Talaat Elshambaky in Journal of applied geodesy, vol 13 n° 1 (January 2019)PermalinkPermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkMachine learning and geographic information systems for large-scale mapping of renewable energy potential / Dan Assouline (2019)PermalinkPermalinkSemantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)PermalinkSpatial decision support in urban environments using machine learning, 3D geo-visualization and semantic integration of multi-source data / Nikolaos Sideris (2019)PermalinkRemote sensing scene classification using multilayer stacked covariance pooling / Nanjun He in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkRobust vehicle detection in aerial images using bag-of-words and orientation aware scanning / Hailing Zhou in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkEstimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery / Lin Chen in Forests, vol 9 n° 10 (October 2018)PermalinkAssessment of Sentinel-1A data for rice crop classification using random forests and support vector machines / Nguyen-Thanh Son in Geocarto international, vol 33 n° 6 (June 2018)PermalinkSpatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)PermalinkAn object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery / Luis Angel Ruiz in Geocarto international, vol 33 n° 5 (May 2018)PermalinkDeep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification / Tao Liu in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkImage classification-based ground filtering of point clouds extracted from UAV-based aerial photos / Volkan Yilmaz in Geocarto international, vol 33 n° 3 (March 2018)PermalinkMapping tree cover with Sentinel-2 data using the Support Vector Machine (SVM) / Anna Mirończuk in Geoinformation issues, Vol 9 n° 1 (2017)PermalinkMultisource remote sensing data classification based on convolutional neural network / Xiaodong Xu in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkActive learning-based optimized training library generation for object-oriented image classification / Rajeswari Balasubramaniam in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)PermalinkAdapting an existing semi-automatized image processing chain to enable Sentinel-2 data classification. / Hiyam Elbadri (2018)PermalinkDecision fusion of SPOT6 and multitemporal Sentinel2 images for urban area detection / Cyril Wendl (2018)PermalinkExploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area / Saygin Abdikan in Geocarto international, vol 33 n° 1 (January 2018)PermalinkExploring the impact of seasonality on urban land-cover mapping using multi-season sentinel-1A and GF-1 WFV images in a subtropical monsoon-climate region / Tao Zhou in ISPRS International journal of geo-information, vol 7 n° 1 (January 2018)PermalinkRéseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne / Jean Ogier du Terrail (2018)PermalinkSpatio-temporal grid mining applied to image classification and cellular automata analysis / Romain Deville (2018)PermalinkPermalinkLearning aggregated features and optimizing model for semantic labeling / Jianhua Wang in The Visual Computer, vol 33 n° 12 (December 2017)PermalinkMultimorphological superpixel model for hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkA relative evaluation of random forests for land cover mapping in an urban area / Di Shi in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 8 (August 2017)PermalinkFusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)PermalinkMonitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms / Lien T.H. Pham in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkSuperpixel-based multitask learning framework for hyperspectral image classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)PermalinkTélédétection et photogrammétrie pour l'étude de la dynamique de l’occupation du sol dans le bassin versant de l’oued Chiba (Cap-Bon, Tunisie) / Anis Gasmi in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)PermalinkToward optimum fusion of thermal hyperspectral and visible images in classification of urban area / Farhad Samadzadegan in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 4 (April 2017)PermalinkEffect of training class label noise on classification performances for land cover mapping with satellite image time series / Charlotte Pelletier in Remote sensing, vol 9 n° 2 (February 2017)PermalinkCartographie de l'occupation des sols à partir de séries temporelles d'images satellitaires à hautes résolutions : identification et traitement des données mal étiquetées / Charlotte Pelletier (2017)PermalinkFusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)PermalinkA hierarchical methodology for urban facade parsing from TLS point clouds / Zhuqiang Li in ISPRS Journal of photogrammetry and remote sensing, vol 123 (January 2017)PermalinkSVM et réseaux neuronaux convolutifs pour la classification de scènes urbaines / Amaury Zarzelli (2017)PermalinkAssessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas / Charlotte Pelletier in Remote sensing of environment, vol 187 (15 December 2016)PermalinkEvaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkAutomatic recognition of long period events from volcano tectonic earthquakes at Cotopaxi volcano / Román A. Lara-Cueva in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkMultiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkSpectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-7 (July 2016)PermalinkFusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)PermalinkPermalinkClassification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkSemi-supervised SVM for individual tree crown species classification / Michele Dalponte in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkAPFiLoc: An Infrastructure-Free Indoor Localization method fusing smartphone inertial sensors, landmarks and map information / Jianga Shang in Sensors, vol 15 n° 10 (October 2015)PermalinkFusion of waveform LiDAR data and hyperspectral imagery for land cover classification / Hongzhou Wang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkMeasuring 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)PermalinkOn spectral unmixing resolution using extended support vector machines / Xiaofeng Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkRegion-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)PermalinkTerraSAR-X dual-pol time-series for mapping of wetland vegetation / Julie Betbeder in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 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)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)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)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)PermalinkContribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (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)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)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)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)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)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)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)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)PermalinkSupervised change detection in satellite imagery using super pixels and relevance feedback / Surender Varma Gadhiraju in Geomatica, vol 68 n° 1 (March 2014)PermalinkAn innovative support vector machine based method for contextual image classification / Rogério Galante Negri in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkAssessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping / Luca Demarchi in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkHyperspectral image classification using nearest feature line embedding approach / Yang-Lang Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)PermalinkA unified framework for land-cover database update and enrichment using satellite imagery / Adrien Gressin (2014)PermalinkUse intermediate results of wrapper band selection methods: A first step toward the optimization of spectral configuration for land cover classifications / Arnaud Le Bris (2014)PermalinkLarge-scale classification of water areas using airborne topographic lidar data / Julien Smeeckaert in Remote sensing of environment, vol 138 (November 2013)PermalinkParcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey / Ugur Alganci in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkInformation content of very high resolution SAR images: study of feature extraction and imaging parameters / Corneliu Dimitru in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)PermalinkTexture classification of PolSAR data based on sparse coding of wavelet polarization textons / Chu He in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)PermalinkSemisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkA shape-based segmentation method for mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkSpectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkBand grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)PermalinkLearning with transductive SVM for semisupervised pixel classification of remote sensing imagery / Ujjwal Maulik in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)PermalinkA graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkModel driven reconstruction of roofs from sparse LIDAR point clouds / A. Henn in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)PermalinkSupport vector machine for spatial variation / C. Andris in Transactions in GIS, vol 17 n° 1 (February 2013)PermalinkPermalinkPermalinkComparaison et évaluation de méthodes d'extraction automatique d'objets sur des images optique et radar / Charlotte Benedetto (2013)PermalinkEvaluation de l'apport de la télédétection radar pour la cartographie des végétations dans le Parc du Pilat / Cécile Cazals (2013)PermalinkPermalinkPredicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest / Marek Jakubowksi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 1 (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)Permalink