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Titre : ISPRS Geospatial Week 2015 : Proceedings Type de document : Actes de congrès Auteurs : Clément Mallet , Éditeur scientifique ; Nicolas Paparoditis , Éditeur scientifique ; Ian J. Dowman, Éditeur scientifique ; Sander J. Oude Elberink, Éditeur scientifique ; Ana-Maria Raimond , Éditeur scientifique ; George Sithole, Éditeur scientifique ; G. Rabatel, Éditeur scientifique ; Franz Rottensteiner, Éditeur scientifique ; Xavier Briottet , Éditeur scientifique ; Sidonie Christophe , Éditeur scientifique ; Arzu Çöltekin, Éditeur scientifique ; G. Patanè, Éditeur scientifique Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2015 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 40-3/W3 Conférence : ISPRS 2015, Geospatial Week : Laserscanning, ISSDQ, CMRT, ISA, GeoVIS, GeoBigData 28/09/2015 03/10/2015 La Grande Motte France ISPRS OA Archives Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse spatiale
[Termes IGN] cartographie d'urgence
[Termes IGN] données localisées
[Termes IGN] données massives
[Termes IGN] drone
[Termes IGN] géovisualisation
[Termes IGN] image hyperspectrale
[Termes IGN] lasergrammétrie
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] qualité des donnéesNote de contenu : - Laserscanning [Traitement de données lidar]
- ISSDQ, International Symposium on Spatial Data Quality [Qualité des données géospatiales]
- CMRT, City Models, Roads and Traffics [Modèles de villes, de routes et de trafic]
- Gi4DM, Geoinformation for Disaster management [Cartographie d'urgence]
- GeoUAV, civil Unmanned Aerial Vehicles for geospatial data acquisition [Drônes civils pour l'acquisition de données géospatiales]
- GeoHyper, Geospatial Hyperspectral [Acquisition et traitement de données hyperspectrales]
- GeoVIS, Geospatial data Visualization [Visualisation de données géospatiales hétérogènes]
- GeoBigData, [Management, traitement et visualisation de données géospatiales massives]
- Geospatial Analysis [Analyse géospatiale]Numéro de notice : 17115 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans Date de publication en ligne : 19/10/2015 En ligne : http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W3/index.htm [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79912 Contient
- Distributed dimensonality-based rendering of lidar point clouds / Mathieu Brédif (2015)
- 3D octree based watertight mesh generation from ubiquitous data / Laurent Caraffa (2015)
- Homogeneous geovisualization of coastal areas from heterogeneous spatio-temporal data / Antoine Masse (2015)
- Extraction of optimal spectral bands using hierarchical band merging out of hyperspectral data / Arnaud Le Bris (2015)
- UAV linear photogrammetry / Vincent Tournadre (2015)
- Generalisation and data quality / Nicolas Regnauld (2015)
- UAV onboard photogrammetry and GPS positioning for earthworks / Mehdi Daakir (2015)
- Coastal digital surface model on low contrast images / Ana-Maria Rosu (2015)
Mediterranean forest species mapping using classification of Hyperion imagery / Georgia Galidaki in Geocarto international, vol 30 n° 1 - 2 (January - February 2015)
[article]
Titre : Mediterranean forest species mapping using classification of Hyperion imagery Type de document : Article/Communication Auteurs : Georgia Galidaki, Auteur ; Ioannis Z. Gitas, Auteur Année de publication : 2015 Article en page(s) : pp 48 - 61 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte de la végétation
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] classification pixellaire
[Termes IGN] forêt méditerranéenne
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectraleRésumé : (auteur) Regional operational forest species mapping is an active research topic that aims to provide the systematic and updatable information necessary for understanding and monitoring the rapidly changing forest environment. In this study, we investigated the potential of satellite hyperspectral imagery in regional forest species mapping by employing a pixel-based and an object-based nearest neighbour classifier in two different Mediterranean study areas. The overall thematic accuracy of the produced maps was assessed using reference data collected in the field and ranged between 0.72 and 0.83. No approach was found to be superior for the study areas. The McNemar test showed no statistically significant difference at the 95% confidence level in the classification accuracies achieved by the two approaches. Both pixel- and object-based approaches provide useful maps, suggesting that regional forest species mapping from space has much potential. Numéro de notice : A2015-245 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.883439 En ligne : https://doi.org/10.1080/10106049.2014.883439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76243
in Geocarto international > vol 30 n° 1 - 2 (January - February 2015) . - pp 48 - 61[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Prediction of the presence of topsoil nitrogen from spaceborne hyperspectral data / Binny Gopal in Geocarto international, vol 30 n° 1 - 2 (January - February 2015)
[article]
Titre : Prediction of the presence of topsoil nitrogen from spaceborne hyperspectral data Type de document : Article/Communication Auteurs : Binny Gopal, Auteur ; Amba Shetty, Auteur ; B. J. Ramya, Auteur Année de publication : 2015 Article en page(s) : pp 82 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] azote
[Termes IGN] détection
[Termes IGN] état de surface du sol
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] Inde
[Termes IGN] régression non linéaire
[Termes IGN] système d'information géographique
[Termes IGN] terre arableRésumé : (auteur) Conventional methods of soil nitrogen extraction are time consuming, expensive and tedious. Remote sensing and Geographical Information System technologies can be used for the rapid and efficient prediction of the presence of soil nitrogen. However, studies are limited by and large to fields of larger and homogeneous units. This research concentrates on the prediction of topsoil nitrogen from harvested, scattered and small-sized agricultural fields of India using hyperspectral data. Spaceborne hyperspectral Hyperion data are used for the prediction of the presence of nitrogen. Multivariate partial least square regression method was used to predict the presence of nitrogen from reflectance. Reflectance data were pretreated using moving average and Savitzky–Golay filters which resulted in moderate prediction of R2 0.65 and 0.63 for calibration and validation, respectively. It can be inferred that Hyperion data can be effectively used for the prediction of the presence of soil nitrogen with a moderate level of accuracy even in case of scattered fields and fields of sizes approximately equal to the spatial resolution of the satellite. Numéro de notice : A2015-246 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.894585 En ligne : https://doi.org/10.1080/10106049.2014.894585 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76244
in Geocarto international > vol 30 n° 1 - 2 (January - February 2015) . - pp 82 - 92[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2015011 RAB Revue Centre de documentation En réserve L003 Disponible A Random Forest class memberships based wrapper band selection criterion : application to hyperspectral / Arnaud Le Bris (2015)
Titre : A Random Forest class memberships based wrapper band selection criterion : application to hyperspectral Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nicolas Paparoditis , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2015 Conférence : IGARSS 2015, International Geoscience And Remote Sensing Symposium 26/07/2015 31/07/2015 Milan Italie Proceedings IEEE Importance : pp 1112 - 1115 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de confiance
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectraleRésumé : (auteur) Hyperspectral imagery generates huge data volumes, consisting of hundreds of contiguous and often highly redundant spectral bands. Difficulties are caused by this high dimensionality. Feature selection (FS) is a possible strategy to reduce the number of bands, consisting in selecting the most relevant bands for a classification problem. It is adapted to the design of superspectral sensor dedicated to specific applications. FS is an optimization problem involving both a metric (that is to say a FS score or criterion measuring the relevance of feature subsets) to optimize and an optimization strategy. In this paper, a wrapper FS score based on Random Forests (RF) and taking into account RF class membership measures was proposed. It was compared to a state-of-the-art wrapper FS score (classification Kappa obtained by RF). Both were then evaluated quantitatively considering both classification performance reached applying different classifiers. An qualitative analysis was also performed to consider the stability/regularity of the selected features along the spectrum. Even though the quantitative evaluation showed little differences between the two tested FS criteria, there seemed to be a trend in favour of the proposed criterion. Taking into account the measures of class membership provided by a RF classifier slightly improved results, regularizing feature selection. Numéro de notice : C2015-022 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2015.7325965 Date de publication en ligne : 12/11/2015 En ligne : http://dx.doi.org/10.1109/IGARSS.2015.7325965 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83168 Remote sensing and image interpretation / Thomas M. Lillesand (2015)
Titre : Remote sensing and image interpretation Type de document : Guide/Manuel Auteurs : Thomas M. Lillesand, Auteur ; Ralph W. Kiefer, Auteur ; Jonathan W. Chipman, Auteur Mention d'édition : 7th edition Editeur : New York, Londres, Hoboken (New Jersey), ... : John Wiley & Sons Année de publication : 2015 Importance : 720 p. Format : 19 x 23 cm ISBN/ISSN/EAN : 978-1-118-34328-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] acquisition de données
[Termes IGN] analyse d'image numérique
[Termes IGN] capteur hyperspectral
[Termes IGN] capteur multibande
[Termes IGN] données lidar
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image thermique
[Termes IGN] Landsat
[Termes IGN] photo-interprétation
[Termes IGN] photogrammétrie
[Termes IGN] radiométrie
[Termes IGN] SPOT
[Termes IGN] système d'information géographique
[Termes IGN] télédétection en hyperfréquenceIndex. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) This book, 7th Edition, is designed to be primarily used in two ways: as a textbook in the introductory courses in remote sensing and image interpretation, and as a reference for the burgeoning number of practitioners who use geospatial information and analysis in their work. Because of the wide range of academic and professional settings in which this book might be used, we have made the discussion “discipline neutral.” In short, anyone involved in geospatial data acquisition and analysis should find this book to be a valuable text and reference. Note de contenu : 1. Concepts and foundations of remote sensing
2. Elements of photographic systems
3. Basic principles of photogrammetry
4. Multispectral, thermal, and hyperspectral senging
5. Earth resource satellite operating in the optical spectrum
6. Microwave ans Lidar sensing
7. Digital image analysis
8. Applications of remote sensing
Appendix A: Radiometric concepts, terminology, and units
Appendix B: Sample coordinate transformation and resampling procedures
Appendix C: Radar signal concepts, terminology, and unitsNuméro de notice : 22527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel de cours Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81514 Spatial-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)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)PermalinkRevue des méthodes de prétraitement des données d'imagerie hyperspectrale acquises depuis un drone / Hachem Agili in Geomatica, vol 68 n° 4 (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)PermalinkAdaptive non-local Euclidean medians sparse unmixing for hyperspectral imagery / Ruyi Feng in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)PermalinkA fast volume-gradient-based band selection method for hyperspectral image / X. Geng in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)PermalinkHyperspectral unmixing with [lq] regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (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)PermalinkTracking seasonal changes of leaf and canopy light use efficiency in a Phlomis fruticosa Mediterranean ecosystem using field measurements and multi-angular satellite hyperspectral imagery / S. Stagakis in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)PermalinkAdaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery / Yanfei Zhong in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 2014)PermalinkHyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing / Mohamed Amine Bendoumi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 2 (October 2014)PermalinkHyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes / Aniruddha Ghosh in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 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)PermalinkClassification of submerged aquatic vegetation in Black River using hyperspectral image analysis / Roshan Pande-Chhetri in Geomatica, vol 68 n° 3 (September 2014)PermalinkRegularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data / Wei Tang 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)PermalinkTélédétection multi-échelle des lacs depuis un aéronef ultraléger motorisé / Y. Akhtman in Géomatique suisse, vol 112 n° 9 (septembre 2014)PermalinkAutomated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor / Helge Aasen in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)PermalinkCombining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades / Caiyun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)Permalink