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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)
[article]
Titre : Vectorisation automatique des forêts dans les minutes de la carte d’état-major du 19e siècle Type de document : Article/Communication Auteurs : Pierre-Alexis Herrault, Auteur ; David Sheeren , Auteur ; Mathieu Fauvel, Auteur ; Martin Paegelow, Auteur Année de publication : 2015 Article en page(s) : pp 35 - 51 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] carte d'Etat-Major
[Termes IGN] classificateur non paramétrique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction automatique
[Termes IGN] forêt
[Termes IGN] vectorisationRésumé : (Auteur) Dans cet article, nous proposons une nouvelle méthode pour extraire automatiquement les forêts dans les cartes d’état-major du 19e siècle numérisées. La démarche proposée est constituée de quatre étapes principales : filtrage de l’image, changement d’espace colorimétrique, identification des forêts à l’aide d’un détecteur non paramétrique (SVDD), post-traitement. La méthode est suffisamment robuste pour prendre en compte la diversité des représentations possibles des forêts dans ces cartes anciennes. Les résultats montrent des performances élevées avec une précision globale de détection obtenue de 95%. Cette approche ouvre de nouvelles perspectives pour les différentes études environnementales incluant une dimension historique. Numéro de notice : A2015-067 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.3166/RIG.25.35-51 Date de publication en ligne : 14/01/2015 En ligne : https://doi.org/10.3166/RIG.25.35-51 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75374
in Revue internationale de géomatique > vol 25 n° 1 (mars - mai 2015) . - pp 35 - 51[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 047-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Density-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)
[article]
Titre : Density-based clustering for data containing two types of points Type de document : Article/Communication Auteurs : Tao Pei, Auteur ; Weiyi Wang, Auteur ; Hengcai Zhang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 175 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification par seuillage sur la limite la plus proche
[Termes IGN] densité d'information
[Termes IGN] échelle d'intensité
[Termes IGN] groupe
[Termes IGN] taxi
[Termes IGN] transport routierRésumé : (Auteur) When only one type of point is distributed in a region, clustered points can be seen as an anomaly. When two different types of points coexist in a region, they overlap at different places with various densities. In such cases, the meaning of a cluster of one type of point may be altered if points of the other type show different densities within the same cluster. If we consider the origins and destinations (OD) of taxicab trips, the clustering of both in the morning may indicate a transportation hub, whereas clustered origins and sparse destinations (a hot spot where taxis are in short supply) could suggest a densely populated residential area. This cannot be identified by previous clustering methods, so it is worthwhile studying a clustering method for two types of points. The concept of two-component clustering is first defined in this paper as a group containing two types of points, at least one of which exhibits clustering. We then propose a density-based method for identifying two-component clusters. The method is divided into four steps. The first estimates the clustering scale of the point data. The second transforms the point data into the 2D density domain, where the x and y axes represent the local density of each type of point around each point, respectively. The third determines the thresholds for extracting the clusters, and the fourth generates two-component clusters using a density-connectivity mechanism. The method is applied to taxicab trip data in Beijing. Three types of two-component clusters are identified: high-density origins and destinations, high-density origins and low-density destinations, and low-density origins and high-density destinations. The clustering results are verified by the spatial relationship between the cluster locations and their land-use types over different periods of the day. Numéro de notice : A2015-577 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.955027 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.955027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77839
in International journal of geographical information science IJGIS > vol 29 n° 2 (February 2015) . - pp 175 - 193[article]Gabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
[article]
Titre : Gabor feature-based collaborative representation for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Sen Jia, Auteur ; Linlin Shen, Auteur ; Qingquan Li, Auteur Année de publication : 2015 Article en page(s) : pp 1118 - 1129 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification spectrale
[Termes IGN] conception collaborative
[Termes IGN] état de l'art
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de Gabor
[Termes IGN] image hyperspectrale
[Termes IGN] précision de la classificationRésumé : (Auteur) Sparse-representation-based classification (SRC) assigns a test sample to the class with minimum representation error via a sparse linear combination of all the training samples, which has successfully been applied to several pattern recognition problems. According to compressive sensing theory, the l1-norm minimization could yield the same sparse solution as the l0 norm under certain conditions. However, the computational complexity of the l1-norm optimization process is often too high for large-scale high-dimensional data, such as hyperspectral imagery (HSI). To make matter worse, a large number of training data are required to cover the whole sample space, which is difficult to obtain for hyperspectral data in practice. Recent advances have revealed that it is the collaborative representation but not the l1-norm sparsity that makes the SRC scheme powerful. Therefore, in this paper, a 3-D Gabor feature-based collaborative representation (3GCR) approach is proposed for HSI classification. When 3-D Gabor transformation could significantly increase the discrimination power of material features, a nonparametric and effective l2-norm collaborative representation method is developed to calculate the coefficients. Due to the simplicity of the method, the computational cost has been substantially reduced; thus, all the extracted Gabor features can be directly utilized to code the test sample, which conversely makes the l2-norm collaborative representation robust to noise and greatly improves the classification accuracy. The extensive experiments on two real hyperspectral data sets have shown higher performance of the proposed 3GCR over the state-of-the-art methods in the literature, in terms of both the classifier complexity and generalization ability from very small training sets. Numéro de notice : A2015-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2334608 En ligne : 10.1109/TGRS.2014.2334608 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75624
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 1118 - 1129[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)
[article]
Titre : Hyperspectral Band Selection by Multitask Sparsity Pursuit Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Qi Wang, Auteur Année de publication : 2015 Article en page(s) : pp 631 -644 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] bande spectrale
[Termes IGN] classification multibande
[Termes IGN] couleur à l'écran
[Termes IGN] image hyperspectrale
[Termes IGN] visualisation de donnéesRésumé : (Auteur) Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the original contents from the raw image cubes. Because of its usefulness, hyperspectral band selection has been successfully applied to many practical applications of hyperspectral remote sensing, such as land cover map generation and color visualization. This paper focuses on groupwise band selection and proposes a new framework, including the following contributions: 1) a smart yet intrinsic descriptor for efficient band representation; 2) an evolutionary strategy to handle the high computational burden associated with groupwise-selection-based methods; and 3) a novel MTSP-based criterion to evaluate the performance of each candidate band combination. To verify the superiority of the proposed framework, experiments have been conducted on both hyperspectral classification and color visualization. Experimental results on three real-world hyperspectral images demonstrate that the proposed framework can lead to a significant advancement in these two applications compared with other competitors. Numéro de notice : A2015-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2326655 En ligne : https://doi.org/10.1109/TGRS.2014.2326655 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75621
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 631 -644[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Sparse 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)
[article]
Titre : Sparse unmixing of hyperspectral data using spectral a priori information Type de document : Article/Communication Auteurs : Wei Tang, Auteur ; Zhenwei Shi, Auteur ; Ying Wu, Auteur ; Changshui Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 770 - 783 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectraleRésumé : (Auteur) Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a challenging task due to the usually high mutual coherence of the spectral library. In this paper, we exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes that some materials in the spectral library are known to exist in the scene. Such information can be obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction method of multipliers, we present a new algorithm, which is termed sparse unmixing using spectral a priori information (SUnSPI), to solve the model. Experimental results on both synthetic and real data demonstrate that the spectral a priori information is beneficial to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance estimation. Numéro de notice : A2015-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2328336 En ligne : https://doi.org/10.1109/TGRS.2014.2328336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75622
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 770 - 783[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Automatic 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)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)PermalinkEtude 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)PermalinkFusion of Lidar and SAR data for land-cover mapping in natural environments / Clara Barbanson (2015)PermalinkImproved 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)PermalinkOpen source geospatial tools / Daniel McInerney (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)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)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)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)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)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)PermalinkCharacterisation 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)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)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)PermalinkAn 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)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)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)PermalinkObservation à l'aide des images satellitaires Landsat TM multidates des impacts du transfert de la gestion forestière aux communautés de base : cas de la commune de Didy, région d'Alaotra-Mangoro, Madagascar / Solofoaisoa Rakotoniaina in Photo interprétation, European journal of applied remote sensing, vol 50 n° 1 (mars 2014)PermalinkSupervised change detection in satellite imagery using super pixels and relevance feedback / Surender Varma Gadhiraju in Geomatica, vol 68 n° 1 (March 2014)PermalinkUL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification / Weiwei Sun in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkFiltering airborne lidar data by modified white top-hat transform with directional edge constraints / Yong Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)PermalinkMultiagent object-based classifier for high spatial resolution imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)PermalinkMultiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkVisual word spatial arrangement for image retrieval and classification / Otávio A.B. Penatti in Pattern recognition, vol 47 n° 2 ([01/02/2014])PermalinkAgricultural field delimitation using active learning and random forests margin / Karim Ghariani (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)PermalinkAnalyse sémantique de nuages de points 3D dans le milieu urbain : sol, façades, objets urbains et accessibilité / Andres Felipe Serna Morales (2014)PermalinkApproches multi-hiérarchiques pour l'analyse d'images de télédétection / Camille Kurtz in Revue Française de Photogrammétrie et de Télédétection, n° 205 (Janvier 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)PermalinkPermalinkCaractérisation et cartographie de la structure forestière à partir d'images satellitaires à très haute résolution spatiale / Benoit Beguet (2014)PermalinkCarte de Kohonen et classification ascendante hiérarchique pour l’analyse de données géohistoriques / Ana-Maria Olteanu-Raimond (2014)PermalinkCombining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)PermalinkComparaison de méthodes d'extraction automatique à partir d'images multispectrales / Valerio Baiocchi in Géomatique expert, n° 96 (01/01/2014)PermalinkConnaissance de la biodiversité végétale / Jan-Bernard Bouzillé (2014)PermalinkContextual classification of lidar data and building object detection in urban areas / Joachim Niemeyer in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkPermalinkFusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification / Markus Gerke in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkGeographic Object-Based Image Analysis: Towards a new paradigm / Thomas Blaschke 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)PermalinkLarge scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)PermalinkMapping a priori defined plant associations using remotely sensed vegetation characteristics / Hans D. Rölofsen in Remote sensing of environment, vol 140 (January 2014)PermalinkMaximum-likelihood estimation for multi-aspect multi-baseline SAR interferometry of urban areas / Michael Schmitt in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkPermalinkPermalinkRobust position information system for e-navigation : Bayesian pool of errors / Thomas Buret in Navigation aérienne, maritime, spatiale, terrestre, vol 62 n° 243 (janvier 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)PermalinkUsing mobile laser scanning data for automated extraction of road markings / Haiyan Guan in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkAn entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkAssessing post-fire regeneration in a Mediterranean mixed forest using lidar data and artificial neural networks / Haifa Debouk in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)PermalinkChange detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data / Marcus Hebel in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)PermalinkHierarchical method of urban building extraction inspired by human perception / Chao Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)PermalinkA parcel shape index for use in land consolidation planning / Demetris Demetriou in Transactions in GIS, vol 17 n° 6 (December 2013)Permalink