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A geometry and texture coupled flexible generalization of urban building models / M. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 1 - 24 Titre : A geometry and texture coupled flexible generalization of urban building models Type de document : Article/Communication Auteurs : M. Zhang, Auteur ; L. Zhang, Auteur ; P. Takis, Auteur ; P. Takis Mathiopoulos, Auteur ; W. Xie, Auteur ; Y. Ding, Auteur ; H. Wang, Auteur Année de publication : 2012 Article en page(s) : pp 1 - 24 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] attribut géomètrique [Termes IGN] CityGML [Termes IGN] données localisées 3D [Termes IGN] façade [Termes IGN] généralisation du bâti [Termes IGN] instance [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle numérique du bâti [Termes IGN] précision géométrique (imagerie) [Termes IGN] réalité virtuelle [Termes IGN] reconstruction 3D du bâti [Termes IGN] texture d'image [Vedettes matières IGN] Généralisation Résumé : (Auteur) In the past, numerous research efforts have focused on generalization of city building models. However, a generic procedure for creating flexible generalization results supporting the fast and efficient update of original building models with various complexities is still an open problem. Moreover, building clusters created in previously published generalization methods are not flexible enough to meet the various requirements for both legible and realistic visualization. Motivated by these observations, this paper proposes a new method for generating a flexible generalization outcome which enables convenient updating of original building models. It also proposes a flexible preprocessing of this generalized information to render a legible and realistic urban scene. This is accomplished by introducing a novel component structure, termed as FEdge, particularly designed for efficiently managing the geometry and texture information in building cluster instances (both original building models and building clusters) during the generalization, visualization and updating processes. Furthermore, a multiple representation structure, referred to as Evolved Buffer-Tree (EBT), is also introduced. The purpose of the EBT is to organize building cluster instances and to employ more flexible LODs for both legible and realistic visualization of urban scenes. FEdge has an intuitive planar shape which can be effectively used in representing rough 3D facade composed by detailed continuous meshes. Each FEdge is given a unique identifier, referred to as FEdge Index. In the proposed generalization scheme, firstly each original building model treated as a building cluster instance is abstracted and presented as FEdge Indices. These FEdge Indices are then used for producing generalized building cluster instances in the EBT portably, and to support convenient model updating and flexible preprocessing of the generalization results for renderable building cluster instances. Secondly, to achieve a legible and realistic visualization of urban scene, the EBT is flexibly assigned diverse LODs maintaining more important legible information than LODs defined in CityGML for 3D building models. To make the generalization more accurate by considering the city roads and districts, an algorithm for automatic road analysis is applied in our clustering and combination. Numerous experiments considering the geometrical and textural complexity of common urban building models, as well as a typical case study of complex city scene with a large number of building models, verify the effectiveness of our generalization method and the dynamic visualization of the generalized urban models. Numéro de notice : A2012-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.008 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31731 [article]

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A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region / Dong Lu ; E. Moran ; et al. in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 26 - 38 Titre : A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; E. Moran, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 26 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse comparative [Termes IGN] bande C [Termes IGN] bande L [Termes IGN] classification dirigée [Termes IGN] image ALOS-PALSAR [Termes IGN] image radar [Termes IGN] image Radarsat [Termes IGN] occupation du sol [Termes IGN] zone tropicale humide Résumé : (Auteur) This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. Numéro de notice : A2012-287 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.010 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31733 [article]

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Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations / J. Travelleti in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 39 - 55 Titre : Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations Type de document : Article/Communication Auteurs : J. Travelleti, Auteur ; C. Delacourt, Auteur ; P. Allemand, Auteur ; Jean-Philippe Malet, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 39 - 55 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alpes-de-haute-provence (04) [Termes IGN] appariement d'images [Termes IGN] effondrement de terrain [Termes IGN] image à très haute résolution [Termes IGN] image multitemporelle [Termes IGN] interpolation [Termes IGN] modèle numérique de terrain [Termes IGN] risque naturel [Termes IGN] surveillance géologique Résumé : (Auteur) The objective of this work is to present a low-cost methodology to monitor the displacement of continuously active landslides from ground-based optical images analyzed with a normalized image correlation technique. The performance of the method is evaluated on a series of images acquired on the Super-Sauze landslide (South French Alps) over the period 2008–2009. The image monitoring system consists of a high resolution optical camera installed on a concrete pillar located on a stable crest in front of the landslide and controlled by a datalogger. The data are processed with a cross-correlation algorithm applied to the full resolution images in the acquisition geometry. Then, the calculated 2D displacement field is orthorectified with a back projection technique using a high resolution DEM interpolated from Airborne Laser Scanning (ALS) data. The heterogeneous displacement field of the landslide is thus characterized in time and space. The performance of the technique is assessed using differential GPS surveys as reference. The sources of error affecting the results are then discussed. The strongest limitations for the application of the technique are related to the meteorological, illumination and ground surface conditions inducing partial or complete loss of coherence among the images. Small movements of the camera and the use of a mono-temporal DEM are the most important factors affecting the accuracy of the orthorectification of the displacement field. As the proposed methodology can be routinely and automatically applied, it offers promising perspectives for operational applications like, for instance, in early warning systems. Numéro de notice : A2012-288 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31734 [article]

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Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions / M. Cutler in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 66 - 77 Titre : Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: An assessment of predictions between regions Type de document : Article/Communication Auteurs : M. Cutler, Auteur ; D. Boyd, Auteur ; Giles M. Foody, Auteur ; A. Vetrivel, Auteur Année de publication : 2012 Article en page(s) : pp 66 - 77 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse comparative [Termes IGN] analyse texturale [Termes IGN] biomasse [Termes IGN] biomasse (combustible) [Termes IGN] Brésil [Termes IGN] classification par réseau neuronal [Termes IGN] déboisement [Termes IGN] forêt tropicale [Termes IGN] image JERS [Termes IGN] image Landsat-TM [Termes IGN] image multibande [Termes IGN] image radar [Termes IGN] Malaisie [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] ondelette [Termes IGN] texture d'image [Termes IGN] Thaïlande Résumé : (Auteur) Quantifying the above ground biomass of tropical forests is critical for understanding the dynamics of carbon fluxes between terrestrial ecosystems and the atmosphere, as well as monitoring ecosystem responses to environmental change. Remote sensing remains an attractive tool for estimating tropical forest biomass but relationships and methods used at one site have not always proved applicable to other locations. This lack of a widely applicable general relationship limits the operational use of remote sensing as a method for biomass estimation, particularly in high biomass ecosystems. Here, multispectral Landsat TM and JERS-1 SAR data were used together to estimate tropical forest biomass at three separate geographical locations: Brazil, Malaysia and Thailand. Texture measures were derived from the JERS-1 SAR data using both wavelet analysis and Grey Level Co-occurrence Matrix methods, and coupled with multispectral data to provide inputs to artificial neural networks that were trained under four different training scenarios and validated using biomass measured from 144 field plots. When trained and tested with data collected from the same location, the addition of SAR texture to multispectral data showed strong correlations with above ground biomass (r = 0.79, 0.79 and 0.84 for Thailand, Malaysia and Brazil respectively). Also, when networks were trained and tested with data from all three sites, the strength of correlation (r = 0.55) was stronger than previously reported results from the same sites that used multispectral data only. Uncertainty in estimating AGB from different allometric equations was also tested but found to have little effect on the strength of the relationships observed. The results suggest that the inclusion of SAR texture with multispectral data can go someway towards providing relationships that are transferable across time and space, but that further work is required if satellite remote sensing is to provide robust and reliable methodologies for initiatives such as Reducing Emissions from Deforestation and Degradation (REDD+). Numéro de notice : A2012-289 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.011 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31735 [article]

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081-2012041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Comparison 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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 78 - 87 Titre : Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points Type de document : Article/Communication Auteurs : Y. Shao, Auteur ; R. Lunetta, Auteur Année de publication : 2012 Article en page(s) : pp 78 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse diachronique [Termes IGN] classification dirigée [Termes IGN] classification et arbre de régression [Termes IGN] classification par réseau neuronal [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image Terra-MODIS [Termes IGN] occupation du sol [Termes IGN] Perceptron multicouche [Termes IGN] série temporelle Résumé : (Auteur) Support vector machine (SVM) was applied for land-cover characterization using MODIS time-series data. Classification performance was examined with respect to training sample size, sample variability, and landscape homogeneity (purity). The results were compared to two conventional nonparametric image classification algorithms: multilayer perceptron neural networks (NN) and classification and regression trees (CART). For 2001 MODIS time-series data, SVM generated overall accuracies ranging from 77% to 80% for training sample sizes from 20 to 800 pixels per class, compared to 67–76% and 62–73% for NN and CART, respectively. These results indicated that SVM’s had superior generalization capability, particularly with respect to small training sample sizes. There was also less variability of SVM performance when classification trials were repeated using different training sets. Additionally, classification accuracies were directly related to sample homogeneity/heterogeneity. The overall accuracies for the SVM algorithm were 91% (Kappa = 0.77) and 64% (Kappa = 0.34) for homogeneous and heterogeneous pixels, respectively. The inclusion of heterogeneous pixels in the training sample did not increase overall accuracies. Also, the SVM performance was examined for the classification of multiple year MODIS time-series data at annual intervals. Finally, using only the SVM output values, a method was developed to directly classify pixel purity. Approximately 65% of pixels within the Albemarle–Pamlico Basin study area were labeled as “functionally homogeneous” with an overall classification accuracy of 91% (Kappa = 0.79). The results indicated a high potential for regional scale operational land-cover characterization applications. Numéro de notice : A2012-290 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.04.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31736 [article]

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An in-depth simulation of EnMAP acquisition geometry / P. Schwind in ISPRS Journal of photogrammetry and remote sensing, vol 70 (June 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 70 (June 2012) . - pp 99 - 106 Titre : An in-depth simulation of EnMAP acquisition geometry Type de document : Article/Communication Auteurs : P. Schwind, Auteur ; R. Muller, Auteur ; G. Palubinskas, Auteur ; T. Storch, Auteur Année de publication : 2012 Article en page(s) : pp 99 - 106 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] capteur spatial [Termes IGN] image EnMAP [Termes IGN] qualité géométrique (image) [Termes IGN] rayonnement infrarouge lointain [Termes IGN] rayonnement proche infrarouge [Termes IGN] simulation d'image Résumé : (Auteur) The future hyperspectral satellite EnMAP (Environmental Mapping and Analysis Program) uses two separate sensors for the acquisition of VNIR and SWIR imagery. Due to their geometric configuration, the SWIR and VNIR instruments map the same positions on the ground with a time delay of 88 ms. Coupled with attitude controller inaccuracies this leads to an estimated co-registration error between SWIR and VNIR higher than the maximum 0.2 pixels designated in the specifications of EnMAP imagery. It is assumed that, by approximating or interpolating the real attitude and geometrically correcting the images, this co-registration error can be significantly reduced. To validate these assumptions, a geometric simulator was developed at the German Aerospace Center DLR which is responsible for the development of the ground segment of EnMAP. The implemented simulator, together with an evaluation of the absolute and relative accuracy, performed using this simulator, are presented in this article. The obtained results demonstrate that the desired co-registration accuracy between SWIR and VNIR imagery can be achieved by using Spline or Chebyshev approximation for the attitude reconstruction but not by using Lagrange interpolation. Numéro de notice : A2012-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.03.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31737 [article]

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