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Street environment change detection from mobile laser scanning point clouds / Wen Xiao in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 107 (September 2015) . - pp 38 - 49 Titre : Street environment change detection from mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Mathieu Brédif , Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2015 Projets : 1-Pas de projet / Article en page(s) : pp 38 - 49 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre k-d [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] milieu urbain [Termes IGN] route [Termes IGN] semis de points [Termes IGN] théorie de Dempster-Shafer Mots-clés libres : Occupancy grids  Point-to-triangle distance Résumé : (auteur) Mobile laser scanning (MLS) has become a popular technique for road inventory, building modelling, infrastructure management, mobility assessment, etc. Meanwhile, due to the high mobility of MLS systems, it is easy to revisit interested areas. However, change detection using MLS data of street environment has seldom been studied. In this paper, an approach that combines occupancy grids and a distance-based method for change detection from MLS point clouds is proposed. Unlike conventional occupancy grids, our occupancy-based method models space based on scanning rays and local point distributions in 3D without voxelization. A local cylindrical reference frame is presented for the interpolation of occupancy between rays according to the scanning geometry. The Dempster–Shafer theory (DST) is utilized for both intra-data evidence fusion and inter-data consistency assessment. Occupancy of reference point cloud is fused at the location of target points and then the consistency is evaluated directly on the points. A point-to-triangle (PTT) distance-based method is combined to improve the occupancy-based method. Because it is robust to penetrable objects, e.g. vegetation, which cause self-conflicts when modelling occupancy. The combined method tackles irregular point density and occlusion problems, also eliminates false detections on penetrable objects. Numéro de notice : A2015-725 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.04.011 Date de publication en ligne : 11/05/2015 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.04.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78374 [article]

TerraSAR-X dual-pol time-series for mapping of wetland vegetation / Julie Betbeder in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 107 (September 2015) . - pp 90 - 98 Titre : TerraSAR-X dual-pol time-series for mapping of wetland vegetation Type de document : Article/Communication Auteurs : Julie Betbeder, Auteur ; Sébastien Rapinel, Auteur ; Samuel Corgne, Auteur ; Eric Pottier, Auteur ; Laurence Hubert-Moy, Auteur Année de publication : 2015 Article en page(s) : pp 90 - 98 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] 1:10.000 [Termes IGN] caractérisation [Termes IGN] carte de la végétation [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données multitemporelles [Termes IGN] données polarimétriques [Termes IGN] image radar moirée [Termes IGN] image TerraSAR-X [Termes IGN] série temporelle [Termes IGN] zone humide Résumé : (auteur) Mapping vegetation formations at a fine scale is crucial for assessing wetland functions and for better landscape management. Identification and characterization of vegetation formations is generally conducted at a fine scale using ecological ground surveys, which are limited to small areas. While optical remotely sensed imagery is limited to cloud-free periods, SAR time-series are used more extensively for wetland mapping and characterization using the relationship between distribution of vegetation formations and flood duration. The aim of this study was to determine the optimal number and key dates of SAR images to be classified to map wetland vegetation formations at a 1:10,000 scale. A series of eight dual-polarization TerraSAR-X images (HH/VV) was acquired in 2013 during dry and wet seasons in temperate climate conditions. One polarimetric parameter was extracted first, the Shannon entropy, which varies with wetland flooding status and vegetation roughness. Classification runs of all the possible combinations of SAR images using different k (number of images) subsets were performed to determine the best combinations of the Shannon entropy images to identify wetland vegetation formations. The classification runs were performed using Support Vector Machine techniques and were then analyzed using the McNemar test to investigate significant differences in the accuracy of all classification runs based on the different image subsets. The results highlight the relevant periods (i.e. late winter, spring and beginning of summer) for mapping vegetation formations, in accordance with ecological studies. They also indicate that a relationship can be established between vegetation formations and hydrodynamic processes with a short time-series of satellite images (i.e. 5 dates). This study introduces a new approach for herbaceous wetland monitoring using SAR polarimetric imagery. This approach estimates the number and key dates required for wetland management (e.g. restoration) and biodiversity studies using remote sensing data. Numéro de notice : A2015-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.05.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78377 [article]

Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia / Loïc Paul Dutrieux in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 107 (September 2015) . - pp 112 - 125 Titre : Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia Type de document : Article/Communication Auteurs : Loïc Paul Dutrieux, Auteur ; Jan Verbesselt, Auteur ; Lammert Kooistra, Auteur ; Martin Herold, Auteur Année de publication : 2015 Article en page(s) : pp 112 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Bolivie [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] forêt tropicale [Termes IGN] image Terra-MODIS [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] sécheresse [Termes IGN] série temporelle [Termes IGN] variabilité Résumé : (auteur) Automatically detecting forest disturbances as they occur can be extremely challenging for certain types of environments, particularly those presenting strong natural variations. Here, we use a generic structural break detection framework (BFAST) to improve the monitoring of forest cover loss by combining multiple data streams. Forest change monitoring is performed using Landsat data in combination with MODIS or rainfall data to further improve the modelling and monitoring. We tested the use of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) with varying spatial aggregation window sizes as well as a rainfall derived index as external regressors. The method was evaluated on a dry tropical forest area in lowland Bolivia where forest cover loss is known to occur, and we validated the results against a set of ground truth samples manually interpreted using the TimeSync environment. We found that the addition of an external regressor allows to take advantage of the difference in spatial extent between human induced and naturally induced variations and only detect the processes of interest. Of all configurations, we found the 13 by 13 km MODIS NDVI window to be the most successful, with an overall accuracy of 87%. Compared with a single pixel approach, the proposed method produced better time-series model fits resulting in increases of overall accuracy (from 82% to 87%), and decrease in omission and commission errors (from 33% to 24% and from 3% to 0% respectively). The presented approach seems particularly relevant for areas with high inter-annual natural variability, such as forests regularly experiencing exceptional drought events. Numéro de notice : A2015-726 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.03.015 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.03.015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78378 [article]

Editorial Theme Issue "Multitemporal remote sensing data analysis" / Clément Mallet in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 107 (September 2015) . - pp 1 - 2 Titre : Editorial Theme Issue "Multitemporal remote sensing data analysis" Type de document : Article/Communication Auteurs : Clément Mallet , Auteur ; Nesrine Chehata , Auteur ; Grégoire Mercier, Auteur Année de publication : 2015 Article en page(s) : pp 1 - 2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] analyse de données [Termes IGN] détection de changement [Termes IGN] données multitemporelles [Termes IGN] données spatiotemporelles Résumé : (auteur) [début] The remote sensing and photogrammetric community has witnessed significant evolution during the last decade and is facing today a new paradigm in data processing and analysis. Indeed, the development of new satellite remote sensing missions leads to an increasing amount of multi-temporal data, with improved spatial, spectral, and temporal resolutions, available at various scales (Berger and Aschbacher, 2012; Belward and Skøien, 2015). In parallel, data becomes available for free, often through dedicated infrastructures, with the opening of satellite and aerial image archives (Landsat and Spot World Heritage, Sentinel missions (Wulder and Coops, 2014)) and with the growing power of benchmark contests, more focused on very high resolution data provision (Benedek and Szirányi, 2009; Rottensteiner et al., 2014; Vallet et al., 2015) or on data fusion (Debes et al., 2014). Consequently, it has never been so easy to collect multiple observations for large areas of the Earth’s surface, which has significantly raised the interest of the scientific community and permitted the development of innovative methods for handling and analysing temporal series of (multimodal) datasets (Bovolo et al., 2013). Numéro de notice : A2015--063 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 1016/j.isprsjprs.2015.07.005 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.07.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83527 [article]