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Unsupervised generative models for data analysis and explainable artificial intelligence / Mohanad Abukmeil (2022)
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 MilanLangues : 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 sourceRésumé : (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 worksNuméro 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 Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images / Chen Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 12 (December 2021)
[article]
Titre : Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images Type de document : Article/Communication Auteurs : Chen Zheng, Auteur ; Yun Zhang, Auteur ; Leiguang Wang, Auteur Année de publication : 2021 Article en page(s) : pp 10555 - 10574 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] granularité d'image
[Termes IGN] segmentation sémantique
[Termes IGN] texture d'imageRésumé : (auteur) Semantic segmentation is one of the most important tasks in remote sensing. However, as spatial resolution increases, distinguishing the homogeneity of each land class and the heterogeneity between different land classes are challenging. The Markov random field model (MRF) is a widely used method for semantic segmentation due to its effective spatial context description. To improve segmentation accuracy, some MRF-based methods extract more image information by constructing the probability graph with pixel or object granularity units, and some other methods interpret the image from different semantic perspectives by building multilayer semantic classes. However, these MRF-based methods fail to capture the relationship between different granularity features extracted from the image and hierarchical semantic classes that need to be interpreted. In this article, a new MRF-based method is proposed to incorporate the multigranularity information and the multilayer semantic classes together for semantic segmentation of remote sensing images. The proposed method develops a framework that builds a hybrid probability graph on both pixel and object granularities and defines a multiclass-layer label field with hierarchical semantic over the hybrid probability graph. A generative alternating granularity inference is suggested to provide the result by iteratively passing and updating information between different granularities and hierarchical semantics. The proposed method is tested on texture images, different remote sensing images obtained by the SPOT5, Gaofen-2, GeoEye, and aerial sensors, and Pavia University hyperspectral image. Experiments demonstrate that the proposed method shows a better segmentation performance than other state-of-the-art methods. Numéro de notice : A2021-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3033293 Date de publication en ligne : 11/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3033293 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99132
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 12 (December 2021) . - pp 10555 - 10574[article]The spatiotemporal implications of urbanization for urban heat islands in Beijing: A predictive approach based on CA–Markov modeling (2004–2050) / Muhammad Amir Siddique in Remote sensing, vol 13 n° 22 (November-2 2021)
[article]
Titre : The spatiotemporal implications of urbanization for urban heat islands in Beijing: A predictive approach based on CA–Markov modeling (2004–2050) Type de document : Article/Communication Auteurs : Muhammad Amir Siddique, Auteur ; Yu Wang, Auteur ; Ninghan Xu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] champ aléatoire de Markov
[Termes IGN] changement d'occupation du sol
[Termes IGN] changement d'utilisation du sol
[Termes IGN] classification et arbre de régression
[Termes IGN] coefficient de corrélation
[Termes IGN] écosystème urbain
[Termes IGN] flore urbaine
[Termes IGN] ilot thermique urbain
[Termes IGN] modèle de simulation
[Termes IGN] Pékin (Chine)
[Termes IGN] planification urbaine
[Termes IGN] série temporelle
[Termes IGN] température au sol
[Termes IGN] urbanisationRésumé : (auteur) The rapid increase in infrastructural development in populated areas has had numerous adverse impacts. The rise in land surface temperature (LST) and its associated damage to urban ecological systems result from urban development. Understanding the current and future LST phenomenon and its relationship to landscape composition and land use/cover (LUC) changes is critical to developing policies to mitigate the disastrous impacts of urban heat islands (UHIs) on urban ecosystems. Using remote sensing and GIS data, this study assessed the multi-scale relationship of LUCC and LST of the cosmopolitan exponentially growing area of Beijing, China. We investigated the impacts of LUC on LST in urban agglomeration for a time series (2004–2019) of Landsat data using Classification and Regression Trees (CART) and a single channel algorithm (SCA), respectively. We built a CA–Markov model to forecast future (2025 and 2050) LUCC and LST spatial patterns. Our results indicate that the cumulative changes in an urban area (UA) increased by about 908.15 km2 (5%), and 11% of vegetation area (VA) decreased from 2004 to 2019. The correlation coefficient of LUCC including vegetation, water bodies, and built-up areas with LST had values of r = −0.155 (p > 0.419), −0.809 (p = 0.000), and 0.526 (p = 0.003), respectively. The results surrounding future forecasts revealed an estimated 2309.55 km2 (14%) decrease in vegetation (urban and forest), while an expansion of 1194.78 km2 (8%) was predicted for a built-up area from 2019 to 2050. This decrease in vegetation cover and expansion of settlements would likely cause a rise of about ~5.74 °C to ~9.66 °C in temperature. These findings strongly support the hypothesis that LST is directly related to the vegetation index. In conclusion, the estimated overall increase of 7.5 °C in LST was predicted from 2019–2050, which is alarming for the urban community’s environmental health. The present results provide insight into sustainable environmental development through effective urban planning of Beijing and other urban hotspots. Numéro de notice : A2021-860 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13224697 Date de publication en ligne : 20/11/2021 En ligne : https://doi.org/10.3390/rs13224697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99074
in Remote sensing > vol 13 n° 22 (November-2 2021) . - n° 4697[article]Geoid determination through the combined least-squares adjustment of GNSS/levelling/gravity networks – a case study in Linyi, China / Dongmei Guo in Survey review, Vol 53 n° 381 (November 2021)
[article]
Titre : Geoid determination through the combined least-squares adjustment of GNSS/levelling/gravity networks – a case study in Linyi, China Type de document : Article/Communication Auteurs : Dongmei Guo, Auteur ; Zhixin Xue, Auteur Année de publication : 2021 Article en page(s) : pp 504 - 512 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse de variance
[Termes IGN] Chine
[Termes IGN] compensation par moindres carrés
[Termes IGN] données GNSS
[Termes IGN] géoïde altimétrique
[Termes IGN] géoïde local
[Termes IGN] levé gravimétrique
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastique
[Termes IGN] pondération
[Termes IGN] réseau de nivellement
[Termes IGN] réseau gravimétriqueRésumé : (Auteur) A detailed discussion of the adjustment problems used to combine GNSS/levelling/gravity network data is provided in this paper. The two primary problems inherent to heterogeneous data networks, namely, parametric models that describe the datums and systematic distortions among the available data sets and stochastic models that describe the observational residuals, are described. For parametric models, a relationship between the transformation parameters and the effects of datums and systematic distortions inherent among different height data types is established based on a least squares criterion. For stochastic models, the stochastic errors in GNSS/levelling/gravity data are evaluated, and a Helmert variance component estimation approach is introduced to refine weighting models. Finally, the proposed model is applied to determine the hybrid geoid in Linyi, China. The numerical results validate the capability and effectiveness of the proposed combined adjustment technique for hybrid geoid computations, revealing an achievable external accuracy of ±1.22 cm compared with GNSS/levelling measurements, which can be increased by 0.44 cm compared with classic adjustments of GNSS/levelling/geoid height data. Numéro de notice : A2021-913 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1842642 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1080/00396265.2020.1842642 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99316
in Survey review > Vol 53 n° 381 (November 2021) . - pp 504 - 512[article]Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior / Maximilian Alexander Coenen in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
[article]
Titre : Pose estimation and 3D reconstruction of vehicles from stereo-images using a subcategory-aware shape prior Type de document : Article/Communication Auteurs : Maximilian Alexander Coenen, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2021 Article en page(s) : pp 27 - 47 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] estimation de pose
[Termes IGN] modèle stochastique
[Termes IGN] problème inverse
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] robotique
[Termes IGN] véhicule automobile
[Termes IGN] vision par ordinateurRésumé : (auteur) The 3D reconstruction of objects is a prerequisite for many highly relevant applications of computer vision such as mobile robotics or autonomous driving. To deal with the inverse problem of reconstructing 3D objects from their 2D projections, a common strategy is to incorporate prior object knowledge into the reconstruction approach by establishing a 3D model and aligning it to the 2D image plane. However, current approaches are limited due to inadequate shape priors and the insufficiency of the derived image observations for a reliable alignment with the 3D model. The goal of this paper is to show how 3D object reconstruction can profit from a more sophisticated shape prior and from a combined incorporation of different observation types inferred from the images. We introduce a subcategory-aware deformable vehicle model that makes use of a prediction of the vehicle type for a more appropriate regularisation of the vehicle shape. A multi-branch CNN is presented to derive predictions of the vehicle type and orientation. This information is also introduced as prior information for model fitting. Furthermore, the CNN extracts vehicle keypoints and wireframes, which are well-suited for model-to-image association and model fitting. The task of pose estimation and reconstruction is addressed by a versatile probabilistic model. Extensive experiments are conducted using two challenging real-world data sets on both of which the benefit of the developed shape prior can be shown. A comparison to state-of-the-art methods for vehicle pose estimation shows that the proposed approach performs on par or better, confirming the suitability of the developed shape prior and probabilistic model for vehicle reconstruction. Numéro de notice : A2021-772 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.006 Date de publication en ligne : 14/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98829
in ISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 27 - 47[article]A topic model based framework for identifying the distribution of demand for relief supplies using social media data / Ting Zhang in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)PermalinkTowards the empirical determination of correlations in terrestrial laser scanner range observations and the comparison of the correlation structure of different scanners / Berit Schmitz in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)PermalinkAdaptive edge preserving maps in Markov random fields for hyperspectral image classification / Chao Pan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)PermalinkOn determination of the geoid from measured gradients of the Earth's gravity field potential / Pavel Novák in Earth-Science Reviews, vol 221 (October 2021)PermalinkA constrained extended Kalman filter based on LS-VCE formulated by condition equations with prediction of cross-covariances / Vahid Mahboub in Survey review, Vol 53 n° 380 (September 2021)PermalinkVariational bayesian compressive multipolarization indoor radar imaging / Van Ha Tang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)PermalinkBackground segmentation in multicolored illumination environments / Nikolas Ladas in The Visual Computer, vol 37 n° 8 (August 2021)PermalinkStructure-aware indoor scene reconstruction via two levels of abstraction / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)PermalinkDeformation analysis of a reference wall towards the uncertainty investigation of terrestrial laser scanners / Berit Schmitz in Journal of applied geodesy, vol 15 n° 3 (July 2021)PermalinkVectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)Permalink