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Spectral variability in hyperspectral unmixing : Multiscale, tensor, and neural network-based approaches / Ricardo Augusto Borsoi (2021)
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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 Study of an integrated pre-processing architecture for smart-imaging-systems, in the context of lowpower computer vision and embedded object detection / Luis Cubero Montealegre (2021)
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Titre : Study of an integrated pre-processing architecture for smart-imaging-systems, in the context of lowpower computer vision and embedded object detection Type de document : Thèse/HDR Auteurs : Luis Cubero Montealegre, Auteur Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 161 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour obtenir le grade de Docteur de l'Université Grenoble Alpes, spécialité : Nano-Electronique et Nano-TechnologiesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] approche hiérarchique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détecteur CMOS
[Termes IGN] détection d'objet
[Termes IGN] détection de contours
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] instrument embarqué
[Termes IGN] intelligence artificielle
[Termes IGN] restauration d'imageIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Embedded Computer vision, as many real application scenarios other areas of artificial intelligence, is facing hardware and power constraints with the rising of edge computing applications. For instance, the object detection problem, consisting in finding different objects of specific classes (types) in an image, turns out to be quite complicated to embed near the image sensor as two complex tasks are required: multi-scale localization and multi-class classification (i.e. identifying bounding boxes that perfectly enclose each object, whatever its size, and labeling the type of the detected object). Today these tasks are mainly often performed on general-purpose desktop machines. Nevertheless, attractive applications like autonomous-driving, augmented reality or video surveillance are urging the need for low-power, low-latency and compact low power devices.The state of the art has approached this challenge by optimizing specific sections of the complete processing-pipeline for a comparable object detection performance. A typical example in the last decade corresponds to minimizing the computing precision, hence the power, to a minimal value. Diminishing the bit-depth or image size has then been studied while implementing pre-processing steps that increase robustness against the loss in bit and image resolution. An algorithm that doesn’t require that kind of pre-processing stage to be programmable is obviously desirable in order to simplify its implementation (e.g. no memory access to learned weights). Another strategy has been to reduce power due to I/O communications amongst different chips or devices thanks to a more exhaustive integration of specialized circuitry and thanks to more efficient memory accesses and mathematical operations.In that context of near-sensor computing, this work points towards a more energy efficient detection pipeline. We target several specific key aspects:1. We try to assess if a dedicated-class-agnostic region proposal algorithm, based on pre-processed low-level features, could replace the typical sliding window approach for object localization in integrated smart imaging systems, allowing to target more efficiently objects in the image. Then, we propose a pipeline that takes into account near image sensor features extraction for Region Proposals with an embedded version of an algorithm called EdgeBoxes.2. We try to assess an optimal type of pre-processing (based on an efficient architecture) that would allow extracting low level features (oriented gradients), and give the best trade-off between power consumption, hardware complexity and object detection performance. Specifically, while being this architecture is fully compatible with region proposal algorithms beyond the sliding window.3. Finally, we try to assess if non-standard, or neuromorphic, image acquisition techniques can be exploited in order to further increase the detection efficiency in real case scenarios.Our methodology relies on behavioral simulations carried out thanks to a custom framework written in Python and C++ code. We propose a hierarchical model (and code architecture) of different image acquisition and processing techniques, and we study their performance through specific metrics related to runtime, memory usage, hardware complexity, I/O data-rate, localization performance and classification performance. We provide comparison with the state of the art and several benchmarks giving guidance to choose one or another architecture depending on the specific needs, and we conclude by stating which one would give, from our perspective, the best trade-offs. Note de contenu : 1. Introduction
2. State of the art
3. Our simulation Framework
4. Region proposals pipeline design
5. Embedded Edge Extraction Circuitry
6. Object Localization benchmarks
7. Dynamic Vision Pre-processing
8. ConclusionNuméro de notice : 28692 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Nano-Electronique et Nano-Technologies : Grenoble : 2021 Organisme de stage : LETI DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03612476/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100289 SUMAC'21: Proceedings of the 3rd Workshop on Structuring and Understanding of Multimedia heritAge Contents / Valérie Gouet-Brunet (2021)
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Titre : SUMAC'21: Proceedings of the 3rd Workshop on Structuring and Understanding of Multimedia heritAge Contents Type de document : Actes de congrès Auteurs : Valérie Gouet-Brunet , Éditeur scientifique ; Margarita Khokhlova
, Éditeur scientifique ; Ronak Kosti, Éditeur scientifique ; Li Weng
, Éditeur scientifique
Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2021 Conférence : SUMAC 2021, 3rd workshop on Structuring and Understanding of Multimedia heritAge Contents 20/10/2021 24/10/2021 Chengdu Chine Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] exploration d'images
[Termes IGN] image numérique
[Termes IGN] image numérisée
[Termes IGN] patrimoine culturel
[Termes IGN] recherche d'image basée sur le contenuNuméro de notice : 13912 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : 10.1145/3475720 En ligne : https://doi.org/10.1145/3475720 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99053 Super-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
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[article]
Titre : Super-resolution of VIIRS-measured ocean color products using deep convolutional neural network Type de document : Article/Communication Auteurs : Xiaoming Liu, Auteur ; Menghua Wang, Auteur Année de publication : 2021 Article en page(s) : pp 114 - 127 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] apprentissage profond
[Termes IGN] bande infrarouge
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] couleur de l'océan
[Termes IGN] image infrarouge couleur
[Termes IGN] image multibande
[Termes IGN] image NPP-VIIRS
[Termes IGN] rayonnementRésumé : (auteur) Since its launch in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has provided high quality global ocean color products, which include normalized water-leaving radiance spectra nLw ( λ ) of six moderate (M) bands (M1–M6) at the wavelengths of 410, 443, 486, 551, 671, and 745 nm with a spatial resolution of 750-m, and one imagery (I) band at a wavelength of 638 nm with a spatial resolution of 375-m. Because the high-resolution I-band measurements are highly correlated spectrally to those of M-band data, it can be used as a guidance to super-resolve the M-band nLw ( λ ) imagery from 750- to 375-m spatial resolution. Super-resolving images from coarse spatial resolution to finer ones have been a field of very active research in recent years. However, no previous studies have been applied to satellite ocean color remote sensing, in particular, for VIIRS ocean color applications. In this study, we employ the deep convolutional neural network (CNN) technique to glean the high-frequency content from the VIIRS I1 band and transfer to super-resolved M-band ocean color images. The network is trained to super-resolve each of the VIIRS six M-bands nLw ( λ ) separately. In our results, the super-resolved (375-m) nLw ( λ ) images are much sharper and show finer spatial structures than the original images. Quantitative evaluations show that biases between the super-resolved and original nLw ( λ ) images are small for all bands. However, errors in the super-resolved nLw ( λ ) images are wavelength-dependent. The smallest error is found in the super-resolved nLw (551) and nLw (671) images, and error increases as the wavelength decreases from 486 to 410 nm. The results show that the networks have the capability to capture the correlations of the M-band and the I1 band images to super-resolved M-band images. Numéro de notice : A2021-031 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992912 Date de publication en ligne : 20/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992912 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96726
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 114 - 127[article]Supplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks / Vivien Sainte Fare Garnot (2021)
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Titre : Supplementary material for: Panoptic segmentation of satellite image time series with convolutional temporal attention networks Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu
, Auteur
Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Conférence : ICCV 2021, IEEE/CVF International Conference on Computer Vision 11/10/2021 17/10/2021 Chengdu Chine programme Importance : pp 1 - 8 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] contour
[Termes IGN] Pastis
[Termes IGN] Perceptron multicouche
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelleRésumé : (auteur) In this appendix, we provide additional information on the PASTIS dataset and our exact model configuration. We also provide complementary qualitative experimental results. Numéro de notice : C2021-024 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98728 Voir aussiDocuments numériques
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