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[n° ou bulletin]
est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1980 -) (1990 -) ![]()
[n° ou bulletin]
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Exemplaires(3)
Code-barres | Cote | Support | Localisation | Section | Disponibilité |
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081-2019091 | RAB | Revue | Centre de documentation | En réserve L003 | Disponible |
081-2019093 | DEP-RECP | Revue | LASTIG | Dépôt en unité | Exclu du prêt |
081-2019092 | DEP-RECF | Revue | Nancy | Dépôt en unité | Exclu du prêt |
Dépouillements
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Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition / Sheng Wang in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
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[article]
Titre : Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition Type de document : Article/Communication Auteurs : Sheng Wang, Auteur ; Andreas Baum, Auteur ; Pablo J. Zarco-Tejada, Auteur ; Carsten Dam-Hansen, Auteur ; Anders Thorseth, Auteur ; Peter Bauer-Gottwein, Auteur ; Filippo Bandini, Auteur ; Monica Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 58 - 71 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] éclairement énergétique
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] image captée par drone
[Termes IGN] image multibande
[Termes IGN] nébulosité
[Termes IGN] réflectance spectrale
[Termes IGN] réflectance végétale
[Termes IGN] tenseurRésumé : (Auteur) Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes. Numéro de notice : A2019-311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.06.017 Date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93336
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 58 - 71[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Addressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
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[article]
Titre : Addressing overfitting on point cloud classification using Atrous XCRF Type de document : Article/Communication Auteurs : Hasan Asy’ari Arief, Auteur ; Ulf Geir Indahl, Auteur ; Geir-Harald Strand, Auteur ; Håvard Tveite, Auteur Année de publication : 2019 Article en page(s) : pp 90 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification automatique
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau neuronal profond
[Termes IGN] semis de pointsRésumé : (Auteur) Advances in techniques for automated classification of point cloud data introduce great opportunities for many new and existing applications. However, with a limited number of labelled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 85.0% in term of overall accuracy, and 71.1% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score. Additionally, transfer learning using the Bergen 2018 dataset, without model retraining, was also performed. Even though our proposal provides a consistent 3% improvement in term of accuracy, more work still needs to be done to alleviate the generalization problem on the domain adaptation and the transfer learning field. Numéro de notice : A2019-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.002 Date de publication en ligne : 11/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.002 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93337
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 90 - 101[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data / Alfonso Fernández-Manso in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
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Titre : Burn severity analysis in Mediterranean forests using maximum entropy model trained with EO-1 Hyperion and LiDAR data Type de document : Article/Communication Auteurs : Alfonso Fernández-Manso, Auteur ; Carmen Quintano, Auteur ; Dar A. Roberts, Auteur Année de publication : 2019 Article en page(s) : pp 102 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] entropie
[Termes IGN] forêt méditerranéenne
[Termes IGN] image EO1-Hyperion
[Termes IGN] incendie de forêtRésumé : (Auteur) All ecosystems and in particular ecosystems in Mediterranean climates are affected by fires. Knowledge of the drivers that most influence burn severity patterns as well an accurate map of post-fire effects are key tools for forest managers in order to plan an adequate post-fire response. Remote sensing data are becoming an indispensable instrument to reach both objectives. This work explores the relative influence of pre-fire vegetation structure and topography on burn severity compared to the impact of post-fire damage level, and evaluates the utility of the Maximum Entropy (MaxEnt) classifier trained with post-fire EO-1 Hyperion data and pre-fire LiDAR to model three levels of burn severity at high accuracy. We analyzed a large fire in central-eastern Spain, which occurred on 16–19 June 2016 in a maquis shrubland and Pinus halepensis forested area. Post-fire hyperspectral Hyperion data were unmixed using Multiple Endmember Spectral Mixture Analysis (MESMA) and five fraction images were generated: char, green vegetation (GV), non-photosynthetic vegetation, soil (NPVS) and shade. Metrics associated with vegetation structure were calculated from pre-fire LiDAR. Post-fire MESMA char fraction image, pre-fire structural metrics and topographic variables acted as inputs to MaxEnt, which built a model and generated as output a suitability surface for each burn severity level. The percentage of contribution of the different biophysical variables to the MaxEnt model depended on the burn severity level (LiDAR-derived metrics had a greater contribution at the low burn severity level), but MaxEnt identified the char fraction image as the highest contributor to the model for all three burn severity levels. The present study demonstrates the validity of MaxEnt as one-class classifier to model burn severity accurately in Mediterranean countries, when trained with post-fire hyperspectral Hyperion data and pre-fire LiDAR. Numéro de notice : A2019-313 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.003 Date de publication en ligne : 14/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93339
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 102 - 118[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Modelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines / Kassel Hingee in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
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Titre : Modelling discontinuous terrain from DSMs using segment labelling, outlier removal and thin-plate splines Type de document : Article/Communication Auteurs : Kassel Hingee, Auteur ; Peter Caccetta, Auteur ; Louis Caccetta, Auteur Année de publication : 2019 Article en page(s) : pp 159 - 171 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] Australie
[Termes IGN] discontinuité
[Termes IGN] filtrage de points
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] segmentation sémantique
[Termes IGN] valeur aberranteRésumé : (Auteur) Models of ground surface elevations are crucial to many applications of remotely sensed data, including estimates of the height relative to ground of non-ground objects, such as buildings and vegetation. In highly engineered regions, such as cities, there are many discontinuities in both the ground surface and the surface of non-ground objects. Sub-metre resolution elevation data for these regions are increasingly available. At these resolutions there is sufficient information and a growing need to improve model accuracies by incorporating discontinuities. Here we provide a new method for generating high resolution models of discontinuous ground surfaces from breakline data and digital surface models derived from remotely sensed data. The method uses segment based filtering, outlier removal and multiresolution thin-plate spline surface fitting. Breaklines are included in the fitted surface using partial derivatives and a breakline-aware method for transferring between different resolutions. We demonstrate our method using elevation data derived from photogrammetry for suburban regions of Perth, Western Australia, and Vaihingen, Germany. We produced ground surface models with noticeable qualitative and quantitative improvements when breaklines are included, at an increased computational cost of approximately 10% when all other parameters remained the same. For LiDAR derived elevations, we report our residual error against a number of other methods recorded using the ISPRS Ground Filtering Test Sites. Numéro de notice : A2019-314 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.004 Date de publication en ligne : 24/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.004 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93340
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 159 - 171[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)
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[article]
Titre : Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2019 Article en page(s) : pp 172 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] détection d'objet
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] image thermique
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] véhicule automobileRésumé : (Auteur) Real-time unmanned aerial vehicles (UAVs)-based thermal infrared images processing, due to high spatial resolution and knowledge of the various infrared radiant energy level distribution of solid bodies, has important applications such as monitoring and control of the various phenomena in different natural situations. One of these applications is monitoring the ground vehicles in cities by using detection or semantic segmentation of them in the thermal images. In this research, our purpose is to improve the performance of deep learning combined model by using Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) specifications for the segmentation of the ground vehicles from UAV-based thermal infrared imagery. The proposed model is studied in three steps. First, designing the proposed model by using an encoder-decoder structure and addition of extracted features from convolutional layers and restricted Boltzmann machine in the network. Second, the implementation of the research goals on four sets of UAV-based thermal infrared imagery named NPU_CS_UAV_IR_DATA that was collected from some streets of China by using FLIR TAU2 thermal infrared sensor in 2017. Finally, analyzing the performance of the proposed model by using five state-of-the-art models in semantic segmentation. The results evaluated the performance of the proposed model as a robust model with the average precision and average processing time of approximately 0.97, and 19.73 s for all datasets, respectively. Numéro de notice : A2019-315 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.009 Date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93341
in ISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 172 - 186[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019091 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019093 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019092 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt