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Spectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches / Ricardo Augusto Borsoi (2021)
Titre : Spectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches Type de document : Thèse/HDR Auteurs : Ricardo Augusto Borsoi, Auteur ; Cédric Richard, Directeur de thèse ; José Carlos Moreira Bermudez, Directeur de thèse Editeur : Nice : Université Côte d'Azur Année de publication : 2021 Importance : 187 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse présentée en vue de l'obtention du grade de docteur science pour l’ingénieur de l’Université Côte d'AzurLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] image multitemporelle
[Termes IGN] réseau antagoniste génératif
[Termes IGN] signature spectrale
[Termes IGN] tenseurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EMs), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process and compromises the quality of the estimated abundances. Therefore, significant effort have been recently dedicated to mitigate the effects of spectral variability in SU. However, many challenges still remain in how to best explore a priori information about the problem in order to improve the quality, the robustness and the efficiency of SU algorithms that account for spectral variability. In this thesis, new strategies are developed to address spectral variability in SU. First, an (over)-segmentation-based multiscale regularization strategy is proposed to explore spatial information about the abundance maps more effectively. New algorithms are then proposed for both semi-supervised and blind SU, leading to improved abundance reconstruction performance at a small computational complexity. Afterwards, three new models are proposed to represent spectral variability of the EMs in SU, using parametric, tensor, and neural network-based representations for EM spectra at each image pixel. The parametric model introduces pixel-dependent scaling factors over a reference EM matrix to model arbitrary spectral variability, while the tensor-based representation allows one to exploit the high-dimensional nature of the data by means of its underlying low-rank structure. Generative neural networks (such as variational autoencoders or generative adversarial networks) finally allow one to model the low-dimensional manifold of the spectral signatures of the materials more effectively. The proposed models are used to devise three new blind SU algorithms, and to perform data augmentation in library-based SU. Finally, we provide a brief overview of work which extends the proposed strategies to new problems in SU and in hyperspectral image analysis. This includes the use of the multiscale abundance regularization in nonlinear SU, modeling spectral variability and accounting for sudden changes when performing SU and change detection of multitemporal hyperspectral images, and also accounting for spectral variability and changes in the multimodal (i.e., hyperspectral and multispectral) image fusion problem. Note de contenu : 1- Introduction
2- Origin of linear mixing model spectral variability in hyperspectral images
3- A ultiscale spatial regularization for fast unmixing with spectral librairies
4- A data dependent multiscale model for spectral unmixing with specral variability
5- Generalized linear mixing model accounting for endmember variability
6- Low-rank tensor modeling for spectral unmixing accounting for spectral variability
7- Deep generative endmembers modeling: An application to unsupervised spectral unmixing
8- Deep generative models for library augmentation in multiple endmember spectral mixture analysis
9- And now for something different...
10- ConclusionsNuméro de notice : 28487 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : thèse de Doctorat : Sciences pour l'Ingénieur : Côte d'Azur : 2021 Organisme de stage : Laboratoire J.-L. Lagrange, Observatoire de la Côte d’Azur DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03253631/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99188
Titre : UAV photogrammetry and remote sensing Type de document : Monographie Auteurs : Fernando Carvajal-Ramírez, Éditeur scientifique ; Francisco Agüera-Vega, Éditeur scientifique ; Patricio Martínez-Carricondo, Éditeur scientifique Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2021 Importance : 258 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-0365-1453-6 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] occupation du sol
[Termes IGN] orthophotographie
[Termes IGN] photogrammétrie aérienne
[Termes IGN] point d'appui
[Termes IGN] reconstruction 3D
[Termes IGN] réseau antagoniste génératif
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] zone tamponRésumé : (éditeur) The concept of remote sensing as a way of capturing information from an object without making contact with it has, until recently, been exclusively focused on the use of Earth observation satellites.The emergence of unmanned aerial vehicles (UAV) with Global Navigation Satellite System (GNSS) controlled navigation and sensor-carrying capabilities has increased the number of publications related to new remote sensing from much closer distances. Previous knowledge about the behavior of the Earth's surface under the incidence different wavelengths of energy has been successfully applied to a large amount of data recorded from UAVs, thereby increasing the special and temporal resolution of the products obtained.More specifically, the ability of UAVs to be positioned in the air at pre-programmed coordinate points; to track flight paths; and in any case, to record the coordinates of the sensor position at the time of the shot and at the pitch, yaw, and roll angles have opened an interesting field of applications for low-altitude aerial photogrammetry, known as UAV photogrammetry. In addition, photogrammetric data processing has been improved thanks to the combination of new algorithms, e.g., structure from motion (SfM), which solves the collinearity equations without the need for any control point, producing a cloud of points referenced to an arbitrary coordinate system and a full camera calibration, and the multi-view stereopsis (MVS) algorithm, which applies an expanding procedure of sparse set of matched keypoints in order to obtain a dense point cloud. The set of technical advances described above allows for geometric modeling of terrain surfaces with high accuracy, minimizing the need for topographic campaigns for georeferencing of such products.This Special Issue aims to compile some applications realized thanks to the synergies established between new remote sensing from close distances and UAV photogrammetry. Note de contenu : 1- Using UAV-based photogrammetry to obtain correlation between the vegetation indices and chemical analysis of agricultural crops
2- Photogrammetry using UAV-mounted GNSS RTK: Georeferencing strategies without GCPs
3- Quality assessment of photogrammetric methods—A workflow for reproducible UAS orthomosaics
4- 3D reconstruction of power lines using UAV images to monitor corridor clearance
5- UAV-based terrain modeling under vegetation in the Chinese Loess Plateau: A deep learning and terrain correction ensemble frameword
6- UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points
7- UAV + BIM: Incorporation of photogrammetric techniques in architectural projects with building information modeling versus classical work processes
8- Structure from motion of multi-angle RPAS imagery complements larger-scale airborne Lidar data for cost-effective snow monitoring in mountain forests
9- Use of UAV-photogrammetry for quasi-vertical wall surveying
10- Deep learning-based single image super-resolution: An investigation for dense scene reconstruction with UAS photogrammetry
11- Mapping heterogeneous urban landscapes from the fusion of digital surface model and unmanned aerial vehicle-based images using adaptive multiscale image segmentation and classificationNuméro de notice : 28664 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-0365-1453-6 En ligne : https://doi.org/10.3390/books978-3-0365-1453-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99850
Titre : Unsupervised vision methods based on image perceptual information Type de document : Thèse/HDR Auteurs : Eric Bazan, Auteur ; Petr Dokladal, Directeur de thèse ; Eva Dokladalova, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2021 Importance : 227 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de doctorat de l'Université Paris Sciences et Lettres, Préparée à MINES ParisTech, spécialité Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] compréhension de l'image
[Termes IGN] contour
[Termes IGN] couleur (variable spectrale)
[Termes IGN] décomposition spectrale
[Termes IGN] filtre de Gabor
[Termes IGN] image captée par drone
[Termes IGN] segmentation d'image
[Termes IGN] texture d'image
[Termes IGN] visionIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis work deals with extracting features and low-level primitives from perceptual image information to understand scenes. Motivated by the needs and problems in Unmanned Aerial Vehicles (UAVs) vision based navigation, we propose novel methods focusing on image understanding problems. This work explores three main pieces of information in an image: intensity, color, and texture. In the first chapter of the manuscript, we work with the intensity information through image contours. We combine this information with human perception concepts, such as the Helmholtz principle and the Gestalt laws, to propose an unsupervised framework for object detection and identification. We validate this methodology in the last stage of the drone navigation, just before the landing. In the following chapters of the manuscript, we explore the color and texture information contained in the images. First, we present an analysis of color and texture as global distributions of an image. This approach leads us to study the Optimal Transport theory and its properties as a true metric for color and texture distributions comparison. We review and compare the most popular similarity measures between distributions to show the importance of a metric with the correct properties such as non-negativity and symmetry. We validate such concepts in two image retrieval systems based on the similarity of color distribution and texture energy distribution. Finally, we build an image representation that exploits the relationship between color and texture information. The image representation results from the image’s spectral decomposition, which we obtain by the convolution with a family of Gabor filters. We present in detail the improvements to the Gabor filter and the properties of the complex color spaces. We validate our methodology with a series of segmentation and boundary detection algorithms based on the computed perceptual feature space. Numéro de notice : 15285 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Morphologie Mathématique : Paris Sciences et Lettres : 2021 Organisme de stage : Centre de Morphologie Mathématique DOI : sans En ligne : https://hal.science/tel-03690309 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101418 Understanding the role of individual units in a deep neural network / David Bau in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 117 n° 48 (1 December 2020)
[article]
Titre : Understanding the role of individual units in a deep neural network Type de document : Article/Communication Auteurs : David Bau, Auteur ; Jun-Yan Zhu, Auteur ; Hendrik Strobelt, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 30071-30078 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] cadre conceptuel
[Termes IGN] détection d'objet
[Termes IGN] réseau antagoniste génératif
[Termes IGN] réseau neuronal convolutif
[Termes IGN] scèneRésumé : (auteur) Deep neural networks excel at finding hierarchical representations that solve complex tasks over large datasets. How can we humans understand these learned representations? In this work, we present network dissection, an analytic framework to systematically identify the semantics of individual hidden units within image classification and image generation networks. First, we analyze a convolutional neural network (CNN) trained on scene classification and discover units that match a diverse set of object concepts. We find evidence that the network has learned many object classes that play crucial roles in classifying scene classes. Second, we use a similar analytic method to analyze a generative adversarial network (GAN) model trained to generate scenes. By analyzing changes made when small sets of units are activated or deactivated, we find that objects can be added and removed from the output scenes while adapting to the context. Finally, we apply our analytic framework to understanding adversarial attacks and to semantic image editing. Numéro de notice : A2020-864 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1073/pnas.1907375117 En ligne : https://doi.org/10.1073/pnas.1907375117 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99086
in Proceedings of the National Academy of Sciences of the United States of America PNAS > vol 117 n° 48 (1 December 2020) . - n° 30071-30078[article]River ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : River ice segmentation with deep learning Type de document : Article/Communication Auteurs : Abhineet Singh, Auteur ; Hayden Kalke, Auteur ; Mark Loewen, Auteur Année de publication : 2020 Article en page(s) : pp 7570 - 7579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] Canada
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] glace
[Termes IGN] image captée par drone
[Termes IGN] rivière
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantiqueRésumé : (auteur) This article deals with the problem of computing surface concentrations for two types of river ice from digital images acquired during freeze-up. It presents the results of attempting to solve this problem using several state-of-the-art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges—very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain. Numéro de notice : A2020-674 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2981082 Date de publication en ligne : 13/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2981082 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96165
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7570 - 7579[article]OpenStreetMap quality assessment using unsupervised machine learning methods / Kent T. Jacobs in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkPermalinkPermalinkUnsupervised satellite image time series analysis using deep learning techniques / Ekaterina Kalinicheva (2020)PermalinkVideo event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)PermalinkUnsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification / Yiting Tao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)PermalinkLearning to diversify deep belief networks for hyperspectral image classification / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)PermalinkPermalinkPermalinkAirborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)PermalinkRoad vectorisation from high-resolution imagery based on dynamic clustering using particle swarm optimisation / Fateme Ameri in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)PermalinkDistinctive 2D and 3D features for automated large-scale scene analysis in urban areas / Martin Weinmann in Computers and graphics, vol 49 (June 2015)PermalinkDetection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning / Andrès Serna in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)PermalinkChange detection in high-resolution land use/land cover geodatabases (at object level) / Emilio Domenech (01/04/2014)PermalinkThe largest empty rectangle containing only a query object in Spatial Databases / Gilberto Gutiérrez in Geoinformatica, vol 18 n° 2 (April 2014)PermalinkMultiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)PermalinkComparaison et évaluation de méthodes d'extraction automatique d'objets sur des images optique et radar / Charlotte Benedetto (2013)PermalinkA framework of region-based spatial relations for non-overlapping features and its application in object based image analysis / Y. Liu in ISPRS Journal of photogrammetry and remote sensing, vol 63 n° 4 (July - August 2008)PermalinkAn application of problem and product ontologies for the revision beach nourishments / Daniel van de Vlag in International journal of geographical information science IJGIS, vol 19 n° 10 (november 2005)PermalinkThe characteristics and interpretability of land surface change and implications for project design / T.L. Sohl in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 4 (April 2004)PermalinkObject-based classification of remote sensing data for change detection / Volker Walter in ISPRS Journal of photogrammetry and remote sensing, vol 58 n° 3-4 (January - June 2004)PermalinkBayesian classification by data augmentation / B. Regguzoni in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)PermalinkICEAGE: interactive clustering and exploration of large and high-dimensional geodata / D. Guo in Geoinformatica, vol 7 n° 3 (September - November 2003)PermalinkJournées data mining spatial et analyse du risque, Versailles, 24 - 25 février 2000 / Sylvain Lassarre (2000)PermalinkConférence d'apprentissage 99, actes de CAP'99, Ecole Polytechnique, Palaiseau, 15-18 juin 1999 / Michèle Sebag (1999)PermalinkAnalyse d'images aériennes haute résolution : détection et modélisation du bâti en zone urbaine / Matthieu Cord (1998)Permalink