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scientifique

Impact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 128 - 138 Titre : Impact of forest disturbance on InSAR surface displacement time series Type de document : Article/Communication Auteurs : Paula M. Bürgi, Auteur ; Rowena B. Lohman, Auteur Année de publication : 2021 Article en page(s) : pp 128 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] déboisement [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] erreur de phase [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] retard ionosphèrique [Termes descripteurs IGN] retard troposphérique [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] Sumatra [Termes descripteurs IGN] surveillance géologique Résumé : (auteur) As interferometric synthetic aperture radar (InSAR) data improve in their global coverage and temporal sampling, studies of ground deformation using InSAR are becoming feasible even in heavily vegetated regions such as the American Pacific Northwest (PNW) and Sumatra. However, ongoing forest disturbance due to logging, wildfires, or disease can introduce time-variable signals which could be misinterpreted as ground displacements. This study constrains the error introduced into InSAR time series in the presence of time-variable forest disturbance using synthetic data. For satellite platforms with randomly distributed orbital positions in time (e.g., Sentinel-1), mid-time series forest disturbance results in random error on the order of 0.2 and 10 cm/year for 1-year secular and time-variable velocities, respectively. If the orbital positions are not randomly distributed in time (e.g., ALOS-1), a biased error on the order of 10 cm/year is introduced to the inferred secular velocity. A time series using real ALOS-1 data near Eugene, OR, USA, shows agreement with the bias estimated by synthetic models. Mitigation of time-variable land cover change effects can be achieved if their timing is known, either through independent observations of surface properties (e.g., Landsat/Sentinel-2) or through the use of more computationally expensive, nonlinear inversions with additional terms for the timing of height changes. Inclusion of these additional terms reduces the potential for misinterpretation of InSAR signals associated with land surface change as ground deformation. Numéro de notice : A2021-032 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992938 date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992938 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96727 [article]

Bayesian-deep-learning estimation of earthquake location from single-station observations / S. Mostafa Mousavi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8211 - 8224 Titre : Bayesian-deep-learning estimation of earthquake location from single-station observations Type de document : Article/Communication Auteurs : S. Mostafa Mousavi, Auteur ; Gregory C. Beroza, Auteur Année de publication : 2020 Article en page(s) : pp 8211 - 8224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification bayesienne [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] épicentre [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] onde sismique [Termes descripteurs IGN] régression [Termes descripteurs IGN] séisme [Termes descripteurs IGN] station d'observation [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] temps de propagation Résumé : (auteur) We present a deep-learning method for a single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multitask temporal convolutional neural network to learn epicentral distance and P travel time from 1-min seismograms. The network estimates epicentral distance and P travel time with mean errors of 0.23 km and 0.03 s and standard deviations of 5.42 km and 0.66 s, respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle and its epistemic uncertainty. This network estimates the direction from which seismic waves arrive at the station with a mean error of 1°. Using this information, we estimate the epicenter, origin time, and depth along with their confidence intervals. We use a global data set of earthquake signals recorded within 1° (~112 km) from the event to build the model and demonstrate its performance. Our model can predict epicenter, origin time, and depth with mean errors of 7.3 km, 0.4 s, and 6.7 km, respectively, at different locations around the world. Our approach can be used for fast earthquake source characterization with a limited number of observations and also for estimating the location of earthquakes that are sparsely recorded—either because they are small or because stations are widely separated. Numéro de notice : A2020-684 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2988770 date de publication en ligne : 06/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2988770 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96209 [article]

Improved indoor positioning based on range-free RSSI fingerprint method / Marcin Uradzinski in Journal of geodetic science, vol 10 n° 1 (janvier 2020)  

Public [article]  inJournal of geodetic science > vol 10 n° 1 (janvier 2020) . - pp 23 - 28 Titre : Improved indoor positioning based on range-free RSSI fingerprint method Type de document : Article/Communication Auteurs : Marcin Uradzinski, Auteur ; Hang Guo, Auteur ; Min YU, Auteur Année de publication : 2020 Article en page(s) : pp 23 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] Bluetooth [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] empreinte [Termes descripteurs IGN] plus proche voisin (algorithme) [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] réseau local sans fil [Termes descripteurs IGN] service fondé sur la position Résumé : (auteur) As the development of modern science and technology, LBS and location-aware computing are increasingly important in the practical applications. Currently, GPS positioning system is a mature positioning technology used widely, but signals are easily absorbed, reflected by buildings, and attenuate seriously. In such situation, GPS positioning is not suitable for using in the indoor environment. Wireless sensor networks, such as ZigBee technology, can provide RSSI (received signal strength indicator) which can be used for positioning, especially indoor positioning, and therefore for location based services (LBS).The authors are focused on the fingerprint database method which is suitable for calculating the coordinates of a pedestrian location. This positioning method can use the signal strength indication between the reference nodes and positioning nodes, and design algorithms for positioning. In the wireless sensor networks, according to whether measuring the distance between the nodes in the positioning process, the positioning modes are divided into two categories which are range-based and range-free positioning modes. This paper describes newly improved indoor positioning method based on RSSI fingerprint database, which is range-free. Presented fingerprint database positioning can provide more accurate positioning results, and the accuracy of establishing fingerprint database will affect the accuracy of indoor positioning. In this paper, we propose a new method about the average threshold and the effective data domain filtering method to optimize the fingerprint database of ZigBee technology. Indoor experiment, which was conducted at the University of Warmia and Mazury, proved that the distance achieved by this system has been extended over 30 meters without decreasing the positioning accuracy. The weighted nearest algorithm was chosen and used to calculate user’s location, and then the results were compared and analyzed. As a result, the positioning accuracy was improved and error did not exceed 0.69 m. Therefore, such system can be easily applied in a bigger space inside the buildings, underground mines or in the other location based services. Numéro de notice : A2020-421 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2020-0004 date de publication en ligne : 04/05/2020 En ligne : https://doi.org/10.1515/jogs-2020-0004 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95479 [article]

Uncertainty in radar emitter classification and clustering / Guillaume Revillon (2019)  

Public Titre : Uncertainty in radar emitter classification and clustering Titre original : Gestion des incertitudes en identification des modes radar Type de document : Thèse/HDR Auteurs : Guillaume Revillon, Auteur ; Charles Soussen, Directeur de thèse ; A. Mohammad- Djafari, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l’Université Paris-Saclay préparée à l’Université Paris-Sud Sciences et Technologies de l’Information et de la Communication (STIC) Spécialité : Traitement du signal et des images Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes descripteurs IGN] approximation [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] écho radar [Termes descripteurs IGN] émetteur [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] modèle de mélange multilinéaire [Termes descripteurs IGN] modulation du signal [Termes descripteurs IGN] probabilités [Termes descripteurs IGN] valeur aberrante Index. décimale : THESE Thèses et HDR Résumé : (auteur) In Electronic Warfare, radar signals identification is a supreme asset for decision making in military tactical situations. By providing information about the presence of threats, classification and clustering of radar signals have a significant role ensuring that countermeasures against enemies are well-chosen and enabling detection of unknown radar signals to update databases. Most of the time, Electronic Support Measures systems receive mixtures of signals from different radar emitters in the electromagnetic environment. Hence a radar signal, described by a pulse-to-pulse modulation pattern, is often partially observed due to missing measurements and measurement errors. The identification process relies on statistical analysis of basic measurable parameters of a radar signal which constitute both quantitative and qualitative data. Many general and practical approaches based on data fusion and machine learning have been developed and traditionally proceed to feature extraction, dimensionality reduction and classification or clustering. However, these algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification/clustering framework that handles both outliers and missing values for any types of data. Here, an approach based on mixture models is developed since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. The proposed approach focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering and inference is processed through a variational Bayesian approximation since the joint posterior distribution of latent variables and parameters is untractable. Some numerical experiments on synthetic and real data show that the proposed method provides more accurate results than standard algorithms. Note de contenu : Introduction 1- State of the art and the selected approach 2- Continuous data 3- Mixed data 4- Temporal evolution data 5- Conclusion and perspectives Numéro de notice : 25703 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Thales, GPI DOI : sans date de publication en ligne : 02/09/2019 En ligne : https://hal.archives-ouvertes.fr/tel-02275817 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94829

Automatic recognition of long period events from volcano tectonic earthquakes at Cotopaxi volcano / Román A. Lara-Cueva in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5247 - 5257 Titre : Automatic recognition of long period events from volcano tectonic earthquakes at Cotopaxi volcano Type de document : Article/Communication Auteurs : Román A. Lara-Cueva, Auteur ; Diego S. Benítez, Auteur ; Enrique V. Carrera, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 5247 - 5257 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] classification par arbre de décision [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] Cotopaxi (volcan) [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du signal [Termes descripteurs IGN] Equateur (état) Résumé : (Auteur) Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high accuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS. Numéro de notice : A2016-897 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2559440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83090 [article]

Signal Processing / Charles L. Byrne (2014)  

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Amélioration de la position GNSS en ville par la méthode des tranchées urbaines / M. Voyer in Géomatique expert, n° 93 (01/07/2013)

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GNSS spoofing detection: Correlating carrier phase with rapid antenna motion / Mark Psiaki in GPS world, vol 24 n° 6 (June 2013)  

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Single-receiver single-channel multi-frequency GNSS integrity: outliers, slips, and ionospheric disturbances / Peter J.G. Teunissen in Journal of geodesy, vol 87 n° 2 (February 2013)  

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A first set of techniques to detect radio frequency interferences and mitigate their impact on SMOS data / R. Castro in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 1 (May 2012)  

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GNSS antenna orientation based on modification of received signal strengths / David Eugen Grimm (2012)  

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Collective detection: enhancing GNSS receiver sensitivity by combining signals from multiple satellites / P. Axelrad in GPS world, vol 21 n° 1 (January 2010)

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Navigation Signal Processing for GNSS Software Receivers / T. Pany (2010)

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Advanced full-waveform lidar data echo detection: assessing quality of derived terrain and tree height models in an alpine coniferous forest / Adrien Chauve in International Journal of Remote Sensing IJRS, vol 30 n° 19 (October 2009)    

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Applications of signal detection theory to Geographic Information Science / A. Griffin in Cartographica, vol 44 n° 3 (September 2009)  

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GNSS / Scott Gleason (2009)

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Traitement statistique du signal / M. Barret (2009)

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ICESat altimetry data product verification at White Sands Space Harbor / L.A. Magruder in IEEE Transactions on geoscience and remote sensing, vol 45 n° 1 (January 2007)

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Le traitement du signal sous Matlab / André Quinquis (2007)

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Fundamentals of global positioning system receivers / James Bao-Yen Tsui (2005)

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Le traitement du signal sous Matlab / André Quinquis (2000)

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Détection et estimation des signaux / D. Declercq (1996)

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Détection et détecteurs de rayonnements optiques / F. Desvignes (1987)

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Radarmétéorologie / H. Sauvageot (1982)

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