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Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery / Yuanheng Sun in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 826 - 840 Titre : Red-edge band vegetation indices for leaf area index estimation from Sentinel-2/MSI imagery Type de document : Article/Communication Auteurs : Yuanheng Sun, Auteur ; Qiming Qin, Auteur ; Huazhong Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 826 - 840 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande rouge [Termes IGN] canopée [Termes IGN] Chine [Termes IGN] image multibande [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] indice foliaire [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] teneur en chlorophylle des feuilles Résumé : (auteur) The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images. Numéro de notice : A2020-069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2940826 Date de publication en ligne : 27/09/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2940826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94615 [article]

Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks / Angel Bueno in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp Titre : Volcano-seismic transfer learning and uncertainty quantification with bayesian neural networks Type de document : Article/Communication Auteurs : Angel Bueno, Auteur ; Carmen Benitez, Auteur ; Silvio De Angelis, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes IGN] apprentissage profond [Termes IGN] classification bayesienne [Termes IGN] classification par réseau neuronal [Termes IGN] forme d'onde [Termes IGN] incertitude des données [Termes IGN] réseau bayesien [Termes IGN] réseau neuronal profond [Termes IGN] Russie [Termes IGN] séisme [Termes IGN] sismologie [Termes IGN] surveillance géologique [Termes IGN] volcanologie [Termes IGN] Washington (Etats-Unis ; état) Résumé : (auteur) Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios. Numéro de notice : A2020-094 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 Date de publication en ligne : 07/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94657 [article]

Tree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 971 - 981 Titre : Tree annotations in LiDAR data using point densities and convolutional neural networks Type de document : Article/Communication Auteurs : Ananya Gupta, Auteur ; Jonathan Byrne, Auteur ; David Moloney, Auteur Année de publication : 2020 Article en page(s) : pp 971 - 981 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] Dublin (Irlande ; ville) [Termes IGN] extraction d'arbres [Termes IGN] image spectrale [Termes IGN] Montréal (Québec) [Termes IGN] segmentation [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] voxel [Termes IGN] zone urbaine Résumé : (auteur) LiDAR provides highly accurate 3-D point clouds. However, data need to be manually labeled in order to provide subsequent useful information. Manual annotation of such data is time-consuming, tedious, and error prone, and hence, in this article, we present three automatic methods for annotating trees in LiDAR data. The first method requires high-density point clouds and uses certain LiDAR data attributes for the purpose of tree identification, achieving almost 90% accuracy. The second method uses a voxel-based 3-D convolutional neural network on low-density LiDAR data sets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelization process. The third method is a scaled version of the PointNet++ method and works directly on outdoor point clouds and achieves an F score of 82.1% on the ISPRS benchmark data set, comparable to the state-of-the-art methods but with increased efficiency. Numéro de notice : A2020-095 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2942201 Date de publication en ligne : 11/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2942201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94658 [article]

Some thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1052 - 1062 Titre : Some thoughts on measuring earthquake deformation using optical imagery Type de document : Article/Communication Auteurs : Min Huang, Auteur ; Yu Zhou, Auteur ; Lejun Lu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1052 - 1062 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement d'images [Termes IGN] artefact [Termes IGN] déformation de la croute terrestre [Termes IGN] image à très haute résolution [Termes IGN] image ALOS [Termes IGN] image optique [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] MNS SRTM [Termes IGN] modèle numérique de surface [Termes IGN] orthorectification [Termes IGN] sismologie Résumé : (auteur) Optical imagery has been proven to be an effective tool for measuring earthquake deformation in continental regions since its first application in the 1999 Izmit earthquake. In this article, we compile and analyze all the earthquakes that have been investigated with optical image matching by 2019, based on which we comment on various issues regarding measuring earthquake deformation with optical imagery. New generations of very high-resolution (VHR) data are effective for earthquake studies, but orthorectification of the VHR images is the major source of error, which is often ignored. We found that the displacements derived from the WorldView images strongly correlate with the errors in the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) that was used in orthorectification. Based on the observed correlation between displacements and topography, we propose a new DEM-based method using the Advanced Land Observing Satellite (ALOS) World 3-D DEM to reduce the orthorectification errors. Combining the published optical data of earthquake deformation, we re-analyze the coseismic slip distribution and shallow slip deficit (SSD). The SSD model states that the coseismic slip in many strike-slip earthquakes decreases in magnitude toward the surface, but this model remains arguable because the interferometric synthetic aperture radar (InSAR)-derived slip is usually not well-constrained at shallow depths due to decorrelation. Because optical matching directly measures the surface slip, we re-examine the slip distribution of 11 strike-slip earthquakes and find that the SSD model may primarily be artifacts in the InSAR measurements. It is therefore of great importance to include the optical data in earthquake studies to constrain coseismic slip inversions. Numéro de notice : A2020-096 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2943192 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2943192 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94669 [article]

Generalized tensor regression for hyperspectral image classification / Jianjun Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1244 - 1258 Titre : Generalized tensor regression for hyperspectral image classification Type de document : Article/Communication Auteurs : Jianjun Liu, Auteur ; Zebin Wu, Auteur ; Liang Xiao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1244 - 1258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] calcul tensoriel [Termes IGN] classification dirigée [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] méthode fondée sur le noyau [Termes IGN] régression [Termes IGN] spectromètre imageur [Termes IGN] tenseur Résumé : (auteur) In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods. Numéro de notice : A2020-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2944989 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2944989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94670 [article]

Mapping precipitable water vapor time series from Sentinel-1 interferometric SAR / Pedro Mateus in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1373 - 1379 Titre : Mapping precipitable water vapor time series from Sentinel-1 interferometric SAR Type de document : Article/Communication Auteurs : Pedro Mateus, Auteur ; João Catalão, Auteur ; Giovanni Nico, Auteur ; Pedro Benevides, Auteur Année de publication : 2020 Article en page(s) : pp 1373 - 1379 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Appalaches [Termes IGN] cartographie [Termes IGN] données GNSS [Termes IGN] image Sentinel-SAR [Termes IGN] interferométrie différentielle [Termes IGN] itération [Termes IGN] méthode des moindres carrés [Termes IGN] modèle atmosphérique [Termes IGN] optimisation (mathématiques) [Termes IGN] phase GNSS [Termes IGN] prévision météorologique [Termes IGN] série temporelle [Termes IGN] vapeur d'eau Résumé : (auteur) In this article, a methodology to retrieve the precipitable water vapor (PWV) from a differential interferometric time series is presented. We used external data provided by atmospheric weather models (e.g., ERA-Interim reanalysis) to constrain the initial state and by Global Navigation Satellite System (GNSS) to phase ambiguities elimination introduced by phase unwrapping algorithm. An iterative least-square is then used to solve the optimization problem. We applied the presented methodology to two time series of differential PWV maps estimated from synthetic aperture radar (SAR) images acquired by the Sentinel-1A, over the southwest part of the Appalachian Mountains (USA). The results were validated using an independent GNSS data set and also compared with atmospheric weather prediction data. The GNSS PWV observations show a strong correlation with the estimated PWV maps with a root-mean-square error less than 1 mm. These results are very encouraging, particularly for the meteorology community, providing crucial information to assimilate into numerical weather models and potentially improve the forecasts. Numéro de notice : A2020-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946077 Date de publication en ligne : 28/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2946077 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94672 [article]