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Fusion 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)
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Titre : Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification Type de document : Article/Communication Auteurs : Markus Gerke, Auteur ; Jing Xiao, Auteur Année de publication : 2014 Article en page(s) : pp 78 - 92 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] classification par arbre de décision
[Termes IGN] conflation
[Termes IGN] densification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] voxelRésumé : (Auteur) Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing. Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline. Numéro de notice : A2014-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32919
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 78 - 92[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible Large scale road network extraction in forested moutainous areas using airborne laser scanning data / António Ferraz (2014)
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Titre : Large scale road network extraction in forested moutainous areas using airborne laser scanning data Type de document : Article/Communication Auteurs : António Ferraz , Auteur ; Clément Mallet
, Auteur ; Nesrine Chehata
, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : IGARSS 2014, International Geoscience And Remote Sensing Symposium 13/07/2014 18/07/2014 Québec Québec - Canada Proceedings IEEE Importance : pp 4315 - 4318 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction du réseau routier
[Termes IGN] forêt alpestre
[Termes IGN] France (administrative)
[Termes IGN] montagne
[Termes IGN] processus ponctuel marqué
[Termes IGN] reconnaissance de formes
[Termes IGN] théorie des graphesRésumé : (auteur) In this work, we present an approach that is able to deal with large-scale road network mapping. While former methods focus on delineating patches of roads without computing a coherent road network, we formulate a very large number of road hypothesis that are pruned using a graph reasoning and weak a priori knowledge on road behavior. The initial solution is computed by means of two machine learning and pattern recognition state-of-the-art methods (namely, Random Forest classification and Marked Point Process) that allow to process very large areas in little time with very satisfactory results. Numéro de notice : C2014-024 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2014.6947444 Date de publication en ligne : 06/11/2014 En ligne : http://dx.doi.org/10.1109/IGARSS.2014.6947444 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92029 Documents numériques
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Large scale road network extraction... - pdf auteurAdobe Acrobat PDFA unified framework for land-cover database update and enrichment using satellite imagery / Adrien Gressin (2014)
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Titre : A unified framework for land-cover database update and enrichment using satellite imagery Type de document : Article/Communication Auteurs : Adrien Gressin , Auteur ; Nicole Vincent, Auteur ; Clément Mallet
, Auteur ; Nicolas Paparoditis
, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2014 Conférence : ICIP 2014, 21st IEEE International Conference on Image Processing 27/10/2014 30/10/2014 Paris France Proceedings IEEE Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] découverte de connaissances
[Termes IGN] image à très haute résolution
[Termes IGN] image Pléiades-HR
[Termes IGN] mise à jour de base de donnéesRésumé : (auteur) 2D land-cover databases (LC-DB) have been established at various levels (global, national or regional scales), various spatial samplings and for various themes of interest (forest, agriculture, urban areas, etc.). However, they exhibit many flaws (limited geometric accuracy, low coverage) and require to be updated with automatic algorithms. Very High Reso-lution satellite imagery offers a suitable solution for setting up such on-purpose algorithms, and a large body of litera-ture has tackled this topic. This paper proposes a framework that is able to deal with both LC-DB update of any kind and their enrichment in case of incomplete DB. The supervised classification-based solution integrates an efficient learning strategy that allows to capture the heterogeneity of the ap-pearances of the various themes of interest. The proposed framework is favorably compared with two state-of-the-art methods, on a reconstructed dataset, composed of sub-metric satellite image patches. Numéro de notice : C2014-032 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2014.7026024 Date de publication en ligne : 29/01/2015 En ligne : https://doi.org/10.1109/ICIP.2014.7026024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92056 Documents numériques
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A unified framework for land-cover... - pdf auteurAdobe Acrobat PDFA methodology to characterize vertical accuracies in lidar-derived products at landscape scales / Wade T. Tinkham in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 8 (August 2013)
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Titre : A methodology to characterize vertical accuracies in lidar-derived products at landscape scales Type de document : Article/Communication Auteurs : Wade T. Tinkham, Auteur ; Chad .m Hoffman, Auteur ; Michael J. Falkowski, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 709 - 716 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] paysage
[Termes IGN] précision altimétriqueRésumé : (Auteur) Light detection and ranging (lidar) is the premier technology for high-resolution elevation measurements in complex landscapes. Lidar error assessments allow for objective interpretation of Digital Elevation Models (DEMs) and products reliant on these layers. The purpose of this study is to spatially estimate the vertical error of a lidar-derived DEM across seven cover types through modeling of field survey data. We use thirty-four variables and ground-based field survey data in a Random Forest regression to predict elevation error. Four variables captured the variability within the lidar errors, with three variables relevant to the distribution of returns within the vegetation and one relating to the terrain form. Good agreement was observed when comparing the survey against the model predictions (u = -0.02 m, s = 0.13 m, and RMSE = 0.14 m). With most lidar products reliant upon accurate production of DEMs, providing spatially explicit assessments of uncertainty at the landscape level will increase user confidence in lidar products. Numéro de notice : A2013-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.8.709 En ligne : https://doi.org/10.14358/PERS.79.8.709 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32563
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 8 (August 2013) . - pp 709 - 716[article]Texture augmented detection of macrophyte species using decision trees / Cameron Proctor in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)
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[article]
Titre : Texture augmented detection of macrophyte species using decision trees Type de document : Article/Communication Auteurs : Cameron Proctor, Auteur ; Yuhong He, Auteur ; Vincent Robinson, Auteur Année de publication : 2013 Article en page(s) : pp 10 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algue
[Termes IGN] classification par arbre de décision
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] macrophyte
[Termes IGN] précision de la classification
[Termes IGN] rivière
[Termes IGN] séparabilité
[Termes IGN] texture d'imageRésumé : (Auteur) Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 x 5 and 13 x 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier. Numéro de notice : A2013-295 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.022 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.02.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32433
in ISPRS Journal of photogrammetry and remote sensing > vol 80 (June 2013) . - pp 10 - 20[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013061 RAB Revue Centre de documentation En réserve L003 Disponible LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy / K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 74 (Novembrer 2012)
PermalinkCombined use of Quickbird and lidar data for mapping a urban environment / N.B. Da Luz in Revue Française de Photogrammétrie et de Télédétection, n° 198 - 199 (Septembre 2012)
PermalinkMapping fragmented agricultural systems in the Sudano-Sahelian environments of Africa using random forest and ensemble metrics of coarse resolution MODIS imagery / E. Vintrou in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)
PermalinkPhenology-based crop classification algorithm and its implications on agricultural water use assessments in California's central valley / L. Zhong in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 8 (August 2012)
PermalinkComparison 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)
PermalinkClassification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)
PermalinkClassification et évolution des tissus urbains à partir de données vectorielles / Anne Puissant in Revue internationale de géomatique, vol 21 n° 4 (décembre 2011 – février 2012)
PermalinkRelevance 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)
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)
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)
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