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Understanding the geodetic signature of large aquifer systems: Example of the Ozark plateaus in central United States / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 127 n° 3 (March 2022)  

Public [article]  inJournal of geophysical research : Solid Earth > vol 127 n° 3 (March 2022) . - n° e2021JB023097 Titre : Understanding the geodetic signature of large aquifer systems: Example of the Ozark plateaus in central United States Type de document : Article/Communication Auteurs : Stacy Larochelle, Auteur ; Kristel Chanard , Auteur ; Luce Fleitout, Auteur ; Jérôme Nicolas Fortin, Auteur ; Adriano Gualandi, Auteur ; Laurent Longuevergne, Auteur ; Paul Rebischung , Auteur ; Sophie Violette, Auteur ; Jean-Philippe Avouac, Auteur Année de publication : 2022 Article en page(s) : n° e2021JB023097 Note générale : bibliographie - financial support : PGSD‐3‐517078‐2018, Natural Sciences and Engineering Research Council of Canada 2019‐2020 STEM Chateaubriand Fellowship, Office for Science and Technology of the Embassy of France in the United States IPGP contribution #4232, Institut de Physique du Globe de Paris Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] analyse en composantes indépendantes [Termes IGN] aquifère [Termes IGN] déformation de la croute terrestre [Termes IGN] données GNSS [Termes IGN] données GRACE [Termes IGN] élasticité [Termes IGN] Etats-Unis [Termes IGN] hydrogéologie [Termes IGN] surcharge hydrologique Résumé : (auteur) The continuous redistribution of water involved in the hydrologic cycle leads to deformation of the solid Earth. On a global scale, this deformation is well explained by the loading imposed by hydrological mass variations and can be quantified to first order with space-based gravimetric and geodetic measurements. At the regional scale, however, aquifer systems also undergo poroelastic deformation in response to groundwater fluctuations. Disentangling these related but distinct 3D deformation fields from geodetic time series is essential to accurately invert for changes in continental water mass, to understand the mechanical response of aquifers to internal pressure changes as well as to correct time series for these known effects. Here, we demonstrate a methodology to accomplish this task by considering the example of the well-instrumented Ozark Plateaus Aquifer System (OPAS) in the central United States. We begin by characterizing the most important sources of groundwater level variations in the spatially heterogeneous piezometer dataset using an Independent Component Analysis. Then, to estimate the associated poroelastic displacements, we project geodetic time series corrected for hydrological loading effects onto the dominant groundwater temporal functions. We interpret the extracted displacements in light of analytical solutions and a 2D model relating groundwater level variations to surface displacements. In particular, the relatively low estimates of elastic moduli inferred from the poroelastic displacements and groundwater fluctuations may be indicative of aquifer layers with a high fracture density. Our findings suggest that OPAS undergoes significant poroelastic deformation, including highly heterogeneous horizontal poroelastic displacements. Numéro de notice : A2022-944 Affiliation des auteurs : UMR IPGP-Géod+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2021JB023097 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1029/2021JB023097 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103155 [article]

Unsupervised generative models for data analysis and explainable artificial intelligence / Mohanad Abukmeil (2022)  

Public Titre : Unsupervised generative models for data analysis and explainable artificial intelligence Type de document : Thèse/HDR Auteurs : Mohanad Abukmeil, Auteur ; Vincenzo Piuri, Directeur de thèse Editeur : Milan [Italie] : Università di Milano Année de publication : 2022 Importance : 194 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat spécialité Informatique, Université de Milan Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] allocation de Dirichlet latente [Termes IGN] analyse en composantes indépendantes [Termes IGN] analyse en composantes principales [Termes IGN] apprentissage automatique [Termes IGN] apprentissage non-dirigé [Termes IGN] modèle stochastique [Termes IGN] navigation autonome [Termes IGN] reconstruction d'image [Termes IGN] réseau antagoniste génératif [Termes IGN] séparation aveugle de source Résumé : (auteur) For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reduction play a substantial role in decomposing the exploratory factors and learning the hidden structures of data, which encompasses the significant features that characterize the categories and trends among data samples in an ordered manner. That is by extracting patterns, differentiating trends, and testing hypotheses to identify anomalies, learning compact knowledge, and performing many different machine learning (ML) tasks such as classification, detection, and prediction. Unsupervised generative learning (UGL) methods are a class of ML characterized by their possibility of analyzing and decomposing latent data, reducing dimensionality, visualizing the manifold of data, and learning representations with limited levels of predefined labels and prior assumptions. Furthermore, explainable artificial intelligence (XAI) is an emerging field of ML that deals with explaining the decisions and behaviors of learned models. XAI is also associated with UGL models to explain the hidden structure of data, and to explain the learned representations of ML models. However, the current UGL models lack large-scale generalizability and explainability in the testing stage, which leads to restricting their potential in ML and XAI applications. To overcome the aforementioned limitations, this thesis proposes innovative methods that integrate UGL and XAI to enable data factorization and dimensionality reduction to improve the generalizability of the learned ML models. Moreover, the proposed methods enable visual explainability in modern applications as anomaly detection and autonomous driving systems. The main research contributions are listed as follows: * A novel overview of UGL models including blind source separation (BSS), manifold learning (MfL), and neural networks (NNs). Also, the overview considers open issues and challenges among each UGL method. * An innovative method to identify the dimensions of the compact feature space via a generalized rank in the application of image dimensionality reduction. * An innovative method to hierarchically reduce and visualize the manifold of data to improve the generalizability in limited data learning scenarios, and computational complexity reduction applications. * An original method to visually explain autoencoders by reconstructing an attention map in the application of anomaly detection and explainable autonomous driving systems. The novel methods introduced in this thesis are benchmarked on publicly available datasets, and they outperformed the state-of-the-art methods considering different evaluation metrics. Furthermore, superior results were obtained with respect to the state-of-the-art to confirm the feasibility of the proposed methodologies concerning the computational complexity, availability of learning data, model explainability, and high data reconstruction accuracy. Note de contenu : 1- Introduction 2- State of the art of unsupervised generative learning (UGL) models 3- Research challenges and open issues of UGL models 4- UGL models for dimensionality reduction and XAI 5- Conclusion and future works Numéro de notice : 15307 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de doctorat : Informatique : Milan : 2022 DOI : 10.13130/abukmeil-mohanad_phd2022-01-24 En ligne : http://dx.doi.org/10.13130/abukmeil-mohanad_phd2022-01-24 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99965

A simplified ICA-based local similarity stereo matching / Suting Chen in The Visual Computer, vol 37 n° 2 (February 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 2 (February 2021) . - pp 411 - 419 Titre : A simplified ICA-based local similarity stereo matching Type de document : Article/Communication Auteurs : Suting Chen, Auteur ; Jinglin Zhang, Auteur ; Meng Jin, Auteur Année de publication : 2021 Article en page(s) : pp 411 - 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse en composantes indépendantes [Termes IGN] appariement d'images [Termes IGN] similitude Résumé : (auteur) Since the existing stereo matching methods may fail in the regions of non-textures, boundaries and tiny details, a simplified independent component correlation algorithm (ICA)-based local similarity stereo matching algorithm is proposed. In order to improve the DispNetC, the proposed algorithm first offers the simplified independent component correlation algorithm (SICA) cost aggregation. Then, the algorithm introduces the matching cost volume pyramid, which simplifies the pre-processing process for the ICA. Also, the SICA loss function is defined. Next, the region-wise loss function combined with the pixel-wise loss function is defined as a local similarity loss function to improve the spatial structure of the disparity map. Finally, the SICA loss function is combined with the local similarity loss function, which is defined to estimate the disparity map and to compensate the edge information of the disparity map. Experimental results on KITTI dataset show that the average absolute error of the proposed algorithm is about 37% lower than that of the DispNetC, and its runtime consuming is about 0.6 s lower than that of GC-Net. Numéro de notice : A2021-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01811-x Date de publication en ligne : 15/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01811-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97286 [article]

Identification and extraction of seasonal geodetic signals due to surface load variations / Stacy Larochelle in Journal of geophysical research : Solid Earth, vol 123 n° 12 (December 2018)  

Public [article]  inJournal of geophysical research : Solid Earth > vol 123 n° 12 (December 2018) . - pp 11031 - 11047 Titre : Identification and extraction of seasonal geodetic signals due to surface load variations Type de document : Article/Communication Auteurs : Stacy Larochelle, Auteur ; Adriano Gualandi, Auteur ; Kristel Chanard , Auteur ; Jean-Philippe Avouac, Auteur Année de publication : 2018 Projets : 3-projet - voir note / Article en page(s) : pp 11031 - 11047 Note générale : bibliographie Funding : King Abdullah City for Science and Technology & NSF. Grant Number: EAR‐1821853 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] analyse en composantes indépendantes [Termes IGN] Arabie [Termes IGN] données géodésiques [Termes IGN] Himalaya [Termes IGN] modèle de déformation tectonique [Termes IGN] Népal [Termes IGN] série temporelle [Termes IGN] surcharge hydrologique [Termes IGN] variation saisonnière Résumé : (auteur) Deformation of the Earth's surface associated with redistributions of continental water mass explains, to first order, the seasonal signals observed in geodetic position time series. Discriminating these seasonal signals from other sources of deformation in geodetic measurements is essential to isolate tectonic signals and to monitor spatio‐temporal variations in continental water storage. We propose a new methodology to identify and extract these seasonal signals. The approach uses a variational Bayesian Independent Component Analysis (vbICA) to extract the seasonal signals and a gravity‐based deformation model to identify which of these signals are caused by surface loading. We test the procedure on two study areas, the Arabian Peninsula and the Nepal Himalaya, and find that the technique successfully extracts the seasonal signals with one or two independent components, depending on whether the load is stationary or moving. The approach is robust to spatial heterogeneities inherent to geodetic measurements and can help extract systematic errors in geodetic products (e.g., draconitic errors). We also discuss how to handle the degree‐1 deformation field present in the geodetic data set but not captured by the gravity‐based model. Numéro de notice : A2018-656 Affiliation des auteurs : Géodésie+Ext (mi2018-2019) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2018JB016607 Date de publication en ligne : 22/11/2018 En ligne : https://doi.org/10.1029/2018JB016607 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93521 [article]

Self-taught feature learning for hyperspectral image classification / Ronald Kemker in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2693 - 2705 Titre : Self-taught feature learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ronald Kemker, Auteur ; Christopher Kanan, Auteur Année de publication : 2017 Article en page(s) : pp 2693 - 2705 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] apprentissage automatique [Termes IGN] classification dirigée [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] image hyperspectrale Résumé : (Auteur) In this paper, we study self-taught learning for hyperspectral image (HSI) classification. Supervised deep learning methods are currently state of the art for many machine learning problems, but these methods require large quantities of labeled data to be effective. Unfortunately, existing labeled HSI benchmarks are too small to directly train a deep supervised network. Alternatively, we used self-taught learning, which is an unsupervised method to learn feature extracting frameworks from unlabeled hyperspectral imagery. These models learn how to extract generalizable features by training on sufficiently large quantities of unlabeled data that are distinct from the target data set. Once trained, these models can extract features from smaller labeled target data sets. We studied two self-taught learning frameworks for HSI classification. The first is a shallow approach that uses independent component analysis and the second is a three-layer stacked convolutional autoencoder. Our models are applied to the Indian Pines, Salinas Valley, and Pavia University data sets, which were captured by two separate sensors at different altitudes. Despite large variation in scene type, our algorithms achieve state-of-the-art results across all the three data sets. Numéro de notice : A2017-467 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2651639 En ligne : https://doi.org/10.1109/TGRS.2017.2651639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86390 [article]

Hyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)  

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Variance components estimation of residual errors in GPS precise positioning / Darko Anđić in Geodetski vestnik, vol 60 n° 3 (September - November 2016)  

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Improved salient feature-based approach for automatically separating photosynthetic and nonphotosynthetic components within terrestrial Lidar point cloud data of forest canopies / Lixia Ma in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

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A phase space reconstruction based single channel ICA algorithm and its application in dam deformation analysis / W. Dai in Survey review, vol 47 n° 345 (November 2015)  

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Polarimetric incoherent target decomposition by means of independent component analysis / Nikola Besic in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

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Density-based clustering for data containing two types of points / Tao Pei in International journal of geographical information science IJGIS, vol 29 n° 2 (February 2015)  

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An abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

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Narrow-band interference suppresion for SAR based on independent component analysis / Feng Zhou in IEEE Transactions on geoscience and remote sensing, vol 51 n° 10 (October 2013)  

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Separation of global time-variable gravity signals into maximally independent components / E. Forootan in Journal of geodesy, vol 86 n° 7 (July 2012)  

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