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A higher order conditional random field model for simultaneous classification of land cover and land use / Lena Albert in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : A higher order conditional random field model for simultaneous classification of land cover and land use Type de document : Article/Communication Auteurs : Lena Albert, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur Année de publication : 2017 Article en page(s) : pp 63 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification à base de connaissances
[Termes IGN] classification automatique
[Termes IGN] classification pixellaire
[Termes IGN] image aérienne
[Termes IGN] inférence
[Termes IGN] occupation du sol
[Termes IGN] prise en compte du contexte
[Termes IGN] relation sémantique
[Termes IGN] utilisation du solRésumé : (Auteur) We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent superpixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intralayer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by interlayer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the superpixels has an influence on the level of detail of the classification result, but also on the degree of smoothing induced by the segmentation method, which is especially beneficial for land cover classes covering large, homogeneous areas. Numéro de notice : A2017-510 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86456
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 63 - 80[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar / Sören Holm in Remote sensing of environment, vol 197 (August 2017)
[article]
Titre : Hybrid three-phase estimators for large-area forest inventory using ground plots, airborne lidar, and space lidar Type de document : Article/Communication Auteurs : Sören Holm, Auteur ; Ross Nelson, Auteur ; Göran Stahl, Auteur Année de publication : 2017 Article en page(s) : pp 85 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] biomasse
[Termes IGN] données ICEsat
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] Etats-Unis
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] placette d'échantillonnage
[Termes IGN] variance
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Previous studies have utilized ground plots, airborne lidar scanning or profiling data, and space lidar profiling data to estimate biomass across large regions, but these studies have failed to take into account the variance components associated with multiple models because the proper variance equations were not available. Previous large-domain studies estimated the variances of their biomass density estimates as the sum of the GLAS sampling variability plus the model variability associated with the models that predict airborne lidar estimates of biomass density (Y) as a function of satellite lidar measurements (X). This approach ignores the additional variability associated with the predictive models used to estimate ground biomass density as a function of airborne lidar measurements. This paper addresses that shortcoming. Analytic variance expressions are provided that include sampling variability and model variability in situations where multiple models are employed to generate estimates of biomass. As an example, the forest biomass of the continental US is estimated, by forest stratum within state, using a space lidar system (ICESat/GLAS). An airborne laser system (ALS) is used as an intermediary to tie the GLAS measurements of forest height to a small subset of US Forest Service (USFS) ground plots by flying the ALS over the ground plots and, independently, over individual GLAS footprints. Two sets of models are employed to relate satellite measurements to the ground plots. The first set of equations relates USFS ground plot estimates of total aboveground dry biomass density (Y1) to spatially coincident ALS forest canopy measurements (X1). The second set of models predicts those ALS canopy height measurements (X1) used in the first set of models to GLAS waveform measurements (X2). The following important conclusions are noted. (1) The variability associated with estimation of the plot-ALS model coefficients is significant and should be included in the overall estimate of biomass density variance. In the continental US, the total variance of mean forest biomass density (98.06 t/ha) increases by a factor of 3.6 ×, i.e., from 1.91 to 6.94 t2/ha2, when plot-ALS model variance is included in the calculation of total variance. (2) State-level results are more variable, but on average, the percent model variance at the state level, i.e., (model variance / total variance) ∗ 100, increases from 16% to 59% when plot-ALS model variance is included. (3) The overall model variance is driven in large part by the number of plots overflown by the ALS and the number of GLAS pulses overflown by the ALS. Given a choice of improving precision by either increasing the number of plot-ALS observations or increasing ALS-GLAS observations, there is no obvious benefit to selecting one over the other. However, typically the number of ground plots overflown is the limiting factor. (4) If heteroskedasticity is evident in either the ground-air or air-satellite models, it can modeled using weighted regression techniques and incorporated into these model variance formulas in straightforward fashion. The results are unambiguous; in a hybrid three-phase sampling framework, both the ground-air and air-satellite model variance components are significant and should be taken into account. Numéro de notice : A2017-655 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.04.004 En ligne : https://doi.org/10.1016/j.rse.2017.04.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87050
in Remote sensing of environment > vol 197 (August 2017) . - pp 85 - 97[article]Modeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
[article]
Titre : Modeling canopy reflectance over sloping terrain based on path length correction Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Ainong Li, Auteur ; Wei Zhao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4597 - 4609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] distorsion du signal
[Termes IGN] figuré du terrain
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle de simulation
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] montagne
[Termes IGN] pente
[Termes IGN] réflectance végétaleRésumé : (Auteur) Sloping terrain induces distortion of canopy reflectance (CR), and the retrieval of biophysical variables from remote sensing data needs to account for topographic effects. We developed a 1-D model (the path length correction (PLC) based model) for simulating CR over sloping terrain. The effects of sloping terrain on single-order and diffuse scatterings are accounted for by PLC and modification of the fraction of incoming diffuse irradiance, respectively. The PLC model was validated via both Monte Carlo and remote sensing image simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slope-induced reflectance distortion with high accuracy (red band: R2 = 0.88; root-mean-square error (RMSE) = 0.0045; relative RMSE (RRMSE) = 15%; near infrared response (NIR) band: R2 = 0.79; RMSE = 0.041; RRMSE = 16%). The comparison of the PLC-simulated results with remote sensing observations acquired by the Landsat8-OLI sensor revealed an accuracy similar to that with the Monte Carlo simulation (red band: R2 = 0.83; RMSE = 0.0053; RRMSE = 13%; NIR band: R2 = 0.77; RMSE = 0.023; RRMSE = 8%). To further validate the PLC model, we used it to implement topographic normalization; the results showed a large reduction in topographic effects after normalization, which implied that the PLC model captures reflectance variations caused by terrain. The PLC model provides a promising tool to improve the simulation of CR and the retrieval of biophysical variables over mountainous regions. Numéro de notice : A2017-500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2694483 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2694483 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86442
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4597 - 4609[article]Impact of GPS differential code bias in dual- and triple-frequency positioning and satellite clock estimation / Haojun Li in GPS solutions, vol 21 n° 3 (July 2017)
[article]
Titre : Impact of GPS differential code bias in dual- and triple-frequency positioning and satellite clock estimation Type de document : Article/Communication Auteurs : Haojun Li, Auteur ; Bofeng Li, Auteur ; Lizhi Lou, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 897 – 903 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] code GPS
[Termes IGN] erreur corrélée au temps
[Termes IGN] erreur systématique
[Termes IGN] géodésie spatiale
[Termes IGN] GPS en mode différentiel
[Termes IGN] positionnement ponctuel précis
[Termes IGN] récepteur bifréquence
[Termes IGN] récepteur trifréquence
[Termes IGN] retard ionosphèriqueRésumé : (auteur) The features and differences of various GPS differential code bias (DCB)s are discussed. The application of these biases in dual- and triple-frequency satellite clock estimation is introduced based on this discussion. A method for estimating the satellite clock error from triple-frequency uncombined observations is presented to meet the need of the triple-frequency uncombined precise point positioning (PPP). In order to evaluate the estimated satellite clock error, the performance of these biases in dual- and triple-frequency positioning is studied. Analysis of the inter-frequency clock bias (IFCB), which is a result of constant and time-varying frequency-dependent hardware delays, in ionospheric-free code-based (P1/P5) single point positioning indicates that its influence on the up direction is more pronounced than on the north and east directions. When the IFCB is corrected, the mean improvements are about 29, 35 and 52% for north, east and up directions, respectively. Considering the contribution of code observations to PPP convergence time, the performance of DCB(P1–P2), DCB(P1–P5) and IFCB in GPS triple-frequency PPP convergence is investigated. The results indicate that the DCB correction can accelerate PPP convergence by means of improving the accuracy of the code observation. The performance of these biases in positioning further verifies the correctness of the estimated dual- and triple-frequency satellite clock error. Numéro de notice : A2017-441 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-016-0578-1 En ligne : https://doi.org/10.1007/s10291-016-0578-1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86358
in GPS solutions > vol 21 n° 3 (July 2017) . - pp 897 – 903[article]Robust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)
[article]
Titre : Robust point cloud classification based on multi-level semantic relationships for urban scenes Type de document : Article/Communication Auteurs : Qing Zhu, Auteur ; Yuan Li, Auteur ; Han Hu, Auteur ; Bo Wu, Auteur Année de publication : 2017 Article en page(s) : pp 86 - 102 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification
[Termes IGN] description multiniveau
[Termes IGN] exploration de données géographiques
[Termes IGN] relation sémantique
[Termes IGN] semis de points
[Termes IGN] voxel
[Termes IGN] zone urbaineRésumé : (Auteur) The semantic classification of point clouds is a fundamental part of three-dimensional urban reconstruction. For datasets with high spatial resolution but significantly more noises, a general trend is to exploit more contexture information to surmount the decrease of discrimination of features for classification. However, previous works on adoption of contexture information are either too restrictive or only in a small region and in this paper, we propose a point cloud classification method based on multi-level semantic relationships, including point–homogeneity, supervoxel–adjacency and class–knowledge constraints, which is more versatile and incrementally propagate the classification cues from individual points to the object level and formulate them as a graphical model. The point–homogeneity constraint clusters points with similar geometric and radiometric properties into regular-shaped supervoxels that correspond to the vertices in the graphical model. The supervoxel–adjacency constraint contributes to the pairwise interactions by providing explicit adjacent relationships between supervoxels. The class–knowledge constraint operates at the object level based on semantic rules, guaranteeing the classification correctness of supervoxel clusters at that level. International Society of Photogrammetry and Remote Sensing (ISPRS) benchmark tests have shown that the proposed method achieves state-of-the-art performance with an average per-area completeness and correctness of 93.88% and 95.78%, respectively. The evaluation of classification of photogrammetric point clouds and DSM generated from aerial imagery confirms the method’s reliability in several challenging urban scenes. Numéro de notice : A2017-347 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.04.022 En ligne : https://dx.doi.org/10.1016/j.isprsjprs.2017.04.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85611
in ISPRS Journal of photogrammetry and remote sensing > vol 129 (July 2017) . - pp 86 - 102[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017071 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017073 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017072 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Decomposition of LiDAR waveforms by B-spline-based modeling / Xiang Shen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkDetection of inconsistencies in geospatial data with geostatistics / Adriana Maria Rocha Trancoso Santos in Boletim de Ciências Geodésicas, vol 23 n° 2 (abr - jun 2017)PermalinkDetermination of a high spatial resolution geopotential model using atomic clock comparisons / Guillaume Lion in Journal of geodesy, vol 91 n° 6 (June 2017)PermalinkDevelopment and Comparison of Species Distribution Models for Forest Inventories / Óscar Rodríguez de Rivera in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)PermalinkForest modelling: the gamma shape mixture model and simulation of tree diameter distributions / Rafał Podlaski in Annals of Forest Science, vol 74 n° 2 (June 2017)PermalinkGPS coordinate time series measurements in Ontario and Quebec, Canada / Hadis Samadi Alinia in Journal of geodesy, vol 91 n° 6 (June 2017)PermalinkIntegrated precipitable water from GPS observations and cimel sunphotometer measurements at CGO Belsk / Michal Kruczyk in Reports on geodesy and geoinformatics, vol 103 n° 1 (June 2017)PermalinkPerformance evaluation of land change simulation models using landscape metrics / Sadeq Dezhkam in Geocarto international, vol 32 n° 6 (June 2017)PermalinkUncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations / Wolfgang Niemeier in Journal of applied geodesy, vol 11 n° 2 (June 2017)Permalink3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)Permalink