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Combined 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)
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Titre : Combined use of Quickbird and lidar data for mapping a urban environment Type de document : Article/Communication Auteurs : N.B. Da Luz, Auteur ; D.J. Dos Santos, Auteur ; A.L. De Mendonça, Auteur ; A.F. Buffara Antunes, Auteur ; H. Araki, Auteur ; Q. Chen, Auteur Année de publication : 2012 Article en page(s) : pp 78 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] carte d'occupation du sol
[Termes IGN] cartographie urbaine
[Termes IGN] classification par arbre de décision
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image Quickbird
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de surface
[Termes IGN] Parana (Brésil)
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) High resolution satellite imagery and airborne Lidar data can characterize the earth surface with unprecedented details in spatial and structure information, respectively. However, most studies have used them separately instead of in combination for land surface mapping. Considering their highly complementary nature, it is critical to investigate whether and how the integration of both can improve the accuracy of land cover and land use (LCLU) mapping. This study explored the use of Quickbird imagery and airborne Lidar data for mapping a complex urban environment in the City of Curitiba, Parana State, Southern Brazil, which is composed of secondary shrub vegetation, grass and climax primary Araucaria forests combined with urban objects such as roads and buildings. Airborne Lidar data were processed to generate DEM, DSM (Digital Surface Model), and DHM (Digital Height Model). The DSM generated from Lidar data were used to orthorectify Quickbird image with photogrammetric skills. A large number of metrics are generated from Lidar DHM, Lidar point cloud, Lidar intensity, and Quickbird imagery using object oriented segmentation approaches. Due to their power in handling non-parametric data, decision-tree based approaches were chosen to select the most relevant metrics and build rules for classification. It was found that the fusion of both types of data with decision trees increased the accuracy by 5 to 15% and resulted in an overall accuracy over 90%. Taking into account the increasing accessibility of Quickbird and airborne Lidar data, it is expected the methodology developed in this study has profound impacts in shifting our current paradigm in LCLU mapping. Numéro de notice : A2012-427 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31873
in Revue Française de Photogrammétrie et de Télédétection > n° 198 - 199 (Septembre 2012) . - pp 78 - 87[article]Automatic detection and segmentation of orchards using very high resolution imagery / Selim Aksoy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
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Titre : Automatic detection and segmentation of orchards using very high resolution imagery Type de document : Article/Communication Auteurs : Selim Aksoy, Auteur ; I. Yalniz, Auteur ; K. Tasdemir, Auteur Année de publication : 2012 Article en page(s) : pp 3117 - 3131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] détection automatique
[Termes IGN] image à très haute résolution
[Termes IGN] image Ikonos
[Termes IGN] image optique
[Termes IGN] image Quickbird
[Termes IGN] segmentation d'image
[Termes IGN] Turquie
[Termes IGN] vergerRésumé : (Auteur) Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data. Numéro de notice : A2012-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2180912 Date de publication en ligne : 31/01/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2180912 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31831
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3117 - 3131[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012081 RAB Revue Centre de documentation En réserve L003 Disponible Phenology-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)
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Titre : Phenology-based crop classification algorithm and its implications on agricultural water use assessments in California's central valley Type de document : Article/Communication Auteurs : L. Zhong, Auteur ; P. Gong, Auteur ; Gregory S. Biging, Auteur Année de publication : 2012 Article en page(s) : pp 799 - 813 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] carte agricole
[Termes IGN] classification par arbre de décision
[Termes IGN] cultures
[Termes IGN] Enhanced vegetation index
[Termes IGN] évapotranspiration
[Termes IGN] fusion d'images
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-TM
[Termes IGN] image Terra-MODIS
[Termes IGN] phénologie
[Termes IGN] segmentation d'imageRésumé : (Auteur) The overarching goal of this study was to map specific crop types in the Central Valley, California and estimate the effect of classification uncertainty on the calculation of crop evapotranspiration (ETc). A phenology-based classification (PBC) approach was developed to identify crop types based on phenological and spectral metrics derived from the time series of Landsat TM/ETM_ imagery. Phenological metrics, calculated by fitting asymmetric double sigmoid functions to temporal profiles of enhanced vegetation index (EVI), were capable of separating crop types with distinct crop calendars. An innovative method was used to compute spectral metrics to represent crops' spectral characteristics at certain phenological stages instead of any specific imaging date. Crop mapping using these metrics showed a stable performance without influences of low-quality data and inter-annual differences in imaging dates. The requirement for ground reference data by the PBC approach was low because classification algorithms were mostly built according to the knowledge on crop calendars and agricultural practices. Techniques including image segmentation, data fusion with MODIS imagery, and decision tree were incorporated to make the approach effective and efficient. Though moderate accuracy (~65.0 percent) was achieved, ETc calculated by the Food and Agriculture Organization (FAO) 56 method showed that the estimate of water use was not likely to be significantly affected by the classification error in PBC. All these advantages imply the strength of the PBC approach in the regular crop mapping of the Central Valley. Numéro de notice : A2012-428 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.8.799 En ligne : https://doi.org/10.14358/PERS.78.8.799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31874
in Photogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 8 (August 2012) . - pp 799 - 813[article]Building detection in complex thorough effective separation of buildings from trees / M. Awrangjeb in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 7 (July 2012)
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Titre : Building detection in complex thorough effective separation of buildings from trees Type de document : Article/Communication Auteurs : M. Awrangjeb, Auteur ; C. Zhang, Auteur ; Clive Simpson Fraser, Auteur Année de publication : 2012 Article en page(s) : pp 729 - 745 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre (flore)
[Termes IGN] détection de contours
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] histogramme
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] seuillage d'imageRésumé : (Auteur) Effective separation of buildings from trees is a major challenge in image-based automatic building detection. This paper presents a three-step method for effective separation of buildings from trees using aerial imagery and lidar data. First, it uses cues such as height to remove objects of low height such as bushes, and width to exclude trees with small horizontal coverage. The height threshold is also used to generate a ground mask where buildings are found to be more separable than in so-called normalized DSM. Second, image entropy and color information are jointly applied to remove easily distinguishable trees. Finally, an innovative rule-based procedure is employed using the edge orientation histogram from the imagery to eliminate false positive candidates. The improved building detection algorithm has been tested on different test areas and it is shown that the algorithm offers high building detection rate in complex scenes which are hilly and densely vegetated. Numéro de notice : A2012-359 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.78.7.729 En ligne : http://dx.doi.org/10.14358/PERS.78.7.729 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31805
in Photogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 7 (July 2012) . - pp 729 - 745[article]A framework for automatic and unsupervised detection of multiple changes in multitemporal images / Francesca Bovolo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)
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Titre : A framework for automatic and unsupervised detection of multiple changes in multitemporal images Type de document : Article/Communication Auteurs : Francesca Bovolo, Auteur ; S. Marchesi, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2012 Article en page(s) : pp 2196 - 2212 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] bande B
[Termes IGN] classification bayesienne
[Termes IGN] détection automatique
[Termes IGN] détection de changement
[Termes IGN] image multibande
[Termes IGN] image multitemporelle
[Termes IGN] seuillage d'imageRésumé : (Auteur) The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images having B spectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach. Numéro de notice : A2012-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2171493 Date de publication en ligne : 21/11/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2171493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31710
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 6 (June 2012) . - pp 2196 - 2212[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012061 RAB Revue Centre de documentation En réserve L003 Disponible Spatial resolution imagery requirements for identifying structure damage in a hurricane disaster: A cognitive approach / S. Battersby in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 6 (June 2012)PermalinkFiltering and segmentation of polarimetric SAR data based on binary partition trees / A. Alonso-Gonzalez in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)PermalinkModelling the Zn emissions from roofing materials at Créteil city scale : Defining a methodology / Emna Sellami-Kaaniche (2012)PermalinkPermalinkSegmentation d'images de façades de bâtiments acquises d'un point de vue terrestre / Jean-Pascal Burochin (2012)PermalinkAmélioration d'une base de données d'empreintes de bâtiments pour la reconstruction 3D : une approche par découpe et fusion / Bruno Vallet in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)PermalinkClassification orientée-objet supervisée d'une forêt avec une sélection guidée d'attributs personnalisés / Olivier de Joinville in Revue Française de Photogrammétrie et de Télédétection, n° 195 (Novembre 2011)PermalinkBuilding footprint database improvement for 3D reconstruction: A split and merge approach and its evaluation / Bruno Vallet in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 5 (September - October 2011)PermalinkBuilding roof modeling from airborne laser scanning data based on level set approach / Kamyoung Kim in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)PermalinkA multispectral and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 7 (July 2011)Permalink