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Conditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
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Titre : Conditional random field and deep feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Fahim Irfan Alam, Auteur ; Jun Zhou, Auteur ; Alan Wee-Chung Liew, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1612 - 1628 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multibande
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] déconvolution
[Termes IGN] données localisées 3D
[Termes IGN] image hyperspectrale
[Termes IGN] voxelRésumé : (Auteur) Image classification is considered to be one of the critical tasks in hyperspectral remote sensing image processing. Recently, a convolutional neural network (CNN) has established itself as a powerful model in classification by demonstrating excellent performances. The use of a graphical model such as a conditional random field (CRF) contributes further in capturing contextual information and thus improving the classification performance. In this paper, we propose a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of CNN and CRF. We use multiple spectral band groups to learn deep features using CNN, and then formulate deep CRF with CNN-based unary and pairwise potential functions to effectively extract the semantic correlations between patches consisting of 3-D data cubes. Furthermore, we introduce a deep deconvolution network that improves the final classification performance. We also introduced a new data set and experimented our proposed method on it along with several widely adopted benchmark data sets to evaluate the effectiveness of our method. By comparing our results with those from several state-of-the-art models, we show the promising potential of our method. Numéro de notice : A2019-131 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2867679 Date de publication en ligne : 20/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2867679 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92461
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1612 - 1628[article]Forest degradation and biomass loss along the Chocó region of Colombia / Victoria Meyer in Carbon Balance and Management, vol 14 (March 2019)
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Titre : Forest degradation and biomass loss along the Chocó region of Colombia Type de document : Article/Communication Auteurs : Victoria Meyer, Auteur ; Sassan Saatchi, Auteur ; António Ferraz , Auteur ; Liang Xu, Auteur ; Duque Alvaro, Auteur ; Mariano Garcia, Auteur ; Mariano Chave, Auteur
Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] biomasse aérienne
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Colombie
[Termes IGN] densité du bois
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] dynamique de la végétation
[Termes IGN] dynamique spatiale
[Termes IGN] forêt tropicale
[Termes IGN] hauteur de la végétation
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] semis de pointsRésumé : (auteur) Background: Wet tropical forests of Chocó, along the Pacific Coast of Colombia, are known for their high plant diversity and endemic species. With increasing pressure of degradation and deforestation, these forests have been prioritized for conservation and carbon offset through Reducing Emissions from Deforestation and forest Degradation (REDD+) mechanisms. We provide the first regional assessment of forest structure and aboveground biomass using measurements from a combination of ground tree inventories and airborne Light Detection and Ranging (Lidar). More than 80,000 ha of lidar samples were collected based on a stratified random sampling to provide a regionally unbiased quantification of forest structure of Chocó across gradients of vegetation structure, disturbance and elevation. We developed a model to convert measurements of vertical structure of forests into aboveground biomass (AGB) for terra firme, wetlands, and mangrove forests. We used the Random Forest machine learning model and a formal uncertainty analysis to map forest height and AGB at 1-ha spatial resolution for the entire pacific coastal region using spaceborne data, extending from the coast to higher elevation of Andean forests.
Results: Upland Chocó forests have a mean canopy height of 21.8 m and AGB of 233.0 Mg/ha, while wetland forests are characterized by a lower height and AGB (13.5 m and 117.5 Mg/a). Mangroves have a lower mean height than upland forests (16.5 m), but have a similar AGB as upland forests (229.9 Mg/ha) due to their high wood density. Within the terra firme forest class, intact forests have the highest AGB (244.3 ± 34.8 Mg/ha) followed by degraded and secondary forests with 212.57 ± 62.40 Mg/ha of biomass. Forest degradation varies in biomass loss from small-scale selective logging and firewood harvesting to large-scale tree removals for gold mining, settlements, and illegal logging. Our findings suggest that the forest degradation has already caused the loss of more than 115 million tons of dry biomass, or 58 million tons of carbon.
Conclusions: Our assessment of carbon stocks and forest degradation can be used as a reference for reporting on the state of the Chocó forests to REDD+ projects and to encourage restoration efforts through conservation and climate mitigation policies.Numéro de notice : A2019-625 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1186/s13021-019-0117-9 Date de publication en ligne : 23/03/2019 En ligne : https://doi.org/10.1186/s13021-019-0117-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95368
in Carbon Balance and Management > vol 14 (March 2019)[article]Geometric comparison and quality evaluation of 3D models of indoor environments / H. Tran in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
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Titre : Geometric comparison and quality evaluation of 3D models of indoor environments Type de document : Article/Communication Auteurs : H. Tran, Auteur ; Kourosh Khoshelham, Auteur ; Allison Kealy, Auteur Année de publication : 2019 Article en page(s) : pp 29 - 39 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] erreur géométrique
[Termes IGN] espace intérieur
[Termes IGN] évaluation des données
[Termes IGN] modèle 3D du site
[Termes IGN] positionnement en intérieur
[Termes IGN] qualité des données
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de points
[Termes IGN] test de performanceRésumé : (Auteur) The increasing demand for automated, cost-effective and time-efficient indoor modelling methods leads to a need for performance evaluation of these methods by assessing the quality of the reconstructed models. In this paper, we introduce a method for geometric comparison of a 3D indoor model with a reference, which is useful not only for evaluating the geometric quality of the model, but also for change detection and temporal analysis of the building. The method provides suitable criteria for the quantitative evaluation of the geometric quality in terms of completeness, correctness, and accuracy. Experimental evaluation on a synthetic dataset and the ISPRS benchmark dataset shows the potential of the proposed method for quantitative evaluation and localization of geometric errors in 3D models of indoor environments. Numéro de notice : A2019-126 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.012 Date de publication en ligne : 19/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92436
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 29 - 39[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning / Jeremy J. Sofonia in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
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Titre : Modelling the effects of fundamental UAV flight parameters on LiDAR point clouds to facilitate objectives-based planning Type de document : Article/Communication Auteurs : Jeremy J. Sofonia, Auteur ; Stuart Phinn, Auteur ; Chris Roelfsema, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 105 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] balayage laser
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] modèle de simulation
[Termes IGN] plan de vol
[Termes IGN] Queensland (Australie)
[Termes IGN] semis de pointsRésumé : (Auteur) Utilised globally across a wide range of applications, the ability to assess and understand LiDAR system capabilities represents an essential component in developing informed decisions on instrument selection and the logistical planning processes associated with site-specific limitations, project objectives and UAV operations. This study employed the new SLAM-based CSIRO “Hovermap” LiDAR system within a purpose-built environment as a testbed to experimentally investigate the interactive effects of fundamental UAV flight parameters on key metrics of LiDAR point clouds. Assessed within a full factorial design at both Site- and Target-levels, the UAV input variables of Pattern, ground Speed and above ground Altitude (AGL) were tested against the point cloud response variables Density, GSD and Accuracy as measured by RMSE and cloud-to-mesh Euclidian distance (‘Deviation’). A novel approach is described wherein the trajectory file of each flight was examined to determine the observed values of the input and response variables, remove noise and facilitate a standardised basis of comparison. Several new terms are introduced including Sampling Effort Variable (SEV, s⋅m−2), Effective Scan Rate (ESR, pts⋅s−1) and Effective Density Rate (EDR, pts⋅m−2⋅s−1) as well as an alternate approach to describe Pattern (s⋅m−1) as a scalar quantity. Reporting significant effects with all response variables at both Site- and Target-levels, the Range of the LiDAR sensor, closely associated with Altitude, presented as the single most important factor. Interestingly, the combination of the independent variables as SEV and EDRpred (‘predicted’ EDR) showed the highest coefficient of determination in the Site-level prediction of Density (AdjR2 = 0.894) and GSD (AdjR2 = 0.978,), respectively, whilst Range best correlated with observed RMSE (AdjR2 = 0.948) and Deviation (AdjR2 = 0.963). Predictive models returned mixed results when evaluated at the Target-level and highlights the need for further investigation to achieve the maximum benefit of high-resolution UAV LiDAR. The results presented here confirm that the CSIRO Hovermap performance is robust and, although variable depending on UAV flight parameters, is predictable and demonstrates the potential value in understanding system performance in harmonised flight planning to achieve project-specific objectives. Numéro de notice : A2019-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.020 Date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92443
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 105 - 118[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt A new waveform decomposition method for multispectral LiDAR / Shalei Song in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
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Titre : A new waveform decomposition method for multispectral LiDAR Type de document : Article/Communication Auteurs : Shalei Song, Auteur ; Binhui Wang, Auteur ; Wei Gong, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 40 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] couvert végétal
[Termes IGN] décomposition de Gauss
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forme d'onde pleine
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Information derived from waveform decomposition of full-waveform light detection and ranging (LiDAR) data has been widely used in vegetation detection and three-dimensional urban terrain modeling to investigate and interpret the structural diversity of surface coverage. Most prevailing waveform decomposition methods involve only a single wavelength, but these methods do not apply to full-waveform multispectral LiDAR (FWMSL) systems that simultaneously acquire spectral and geometric information. In this paper, we propose a new multispectral waveform decomposition (MSWD) method in order to explore the potential advantages of the FWMSL system. Both simulated data and measured data from our FWMSL system were used to evaluate the performance of the proposed method. The coefficient of determination (R2), root mean square error (RMSE), and relative error (rRMSE) metrics suggest that the decomposition results derived from MSWD exhibit a comparable overall fitting accuracy as a single wavelength waveform decomposition (SWWD) method. We also propose a new evaluation indicator, relative neighbor distance error (RNDE), to represent the relative error in the distance between adjacent targets. The simulation results present clear superiority of MSWD over SWWD in terms of discovering weak or overlapping components and retrieving accurate waveform parameters. The experimental results demonstrated a considerable improvement in RNDE (0.0100–0.0610) over the prevailing SWWD method (0.0566–0.2833). Unlike SWWD, MSWD initializes waveform components using mutually complementary wavelengths thus delivering higher completeness and accuracy. MSWD can be extended to other FWMSL or full-waveform hyperspectral LiDAR systems with additional wavelengths. Numéro de notice : A2019-127 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.014 Date de publication en ligne : 22/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.014 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92438
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 40 - 49[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt SEH-SDB : a semantically enriched historical spatial database for documentation and preservation of monumental heritage based on CityGML / Reda Yaagoubi in Applied geomatics, vol 11 n° 1 (March 2019)
PermalinkStem-leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data / Shichao Jin in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
PermalinkUAS lidar for ecological restoration of wetlands / Marie de Boisvilliers in GIM international, Vol 33 n° 2 (March - April 2019)
PermalinkUtilizing a discrete global grid system for handling point clouds with varying locations, times, and levels of detail / Neeraj Sirdeshmukh in Cartographica, vol 54 n° 1 (Spring 2019)
PermalinkPredicting tree diameter using allometry described by non-parametric locally-estimated copulas from tree dimensions derived from airborne laser scanning / Qing Xu in Forest ecology and management, vol 434 (28 February 2019)
PermalinkUsing LiDAR to develop high-resolution reference models of forest structure and spatial pattern / Haley L. Wiggins in Forest ecology and management, vol 434 (28 February 2019)
PermalinkLeaf area density from airborne LiDAR: Comparing sensors and resolutions in a temperate broadleaf forest ecosystem / Aaron G. Kamoske in Forest ecology and management, vol 433 (15 February 2019)
PermalinkA simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions / Syed Adnan in Forest ecology and management, vol 433 (15 February 2019)
PermalinkA derivative-free optimization-based approach for detecting architectural symmetries from 3D point clouds / Fan Xue in ISPRS Journal of photogrammetry and remote sensing, vol 148 (February 2019)
PermalinkDiffusion and inpainting of reflectance and height LiDAR orthoimages / Pierre Biasutti in Computer Vision and image understanding, vol 179 (February 2019)
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