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A continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2280 Titre : A continuous change tracker model for remote sensing time series reconstruction Type de document : Article/Communication Auteurs : Yangjian Zhang, Auteur ; Li Wang, Auteur ; Yuanhuizi He, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de filtrage [Termes IGN] analyse harmonique [Termes IGN] compression d'image [Termes IGN] détection de changement [Termes IGN] extraction de traits caractéristiques [Termes IGN] Leaf Area Index [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] production primaire brute [Termes IGN] reconstruction d'image [Termes IGN] réflectance de surface [Termes IGN] série temporelle Résumé : (auteur) It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions. Numéro de notice : A2022-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092280 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100662 [article]

Deep learning-based point cloud compression / Maurice Quach (2022)  

Public Titre : Deep learning-based point cloud compression Titre original : Compression de nuages de points par apprentissage profond Type de document : Thèse/HDR Auteurs : Maurice Quach, Auteur ; Frédéric Dufaux, Directeur de thèse ; Giuseppe Valenzise, Directeur de thèse Editeur : Bures-sur-Yvette : Université Paris-Saclay Année de publication : 2022 Importance : 165 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de Doctorat de l'Université de Saclay, spécialité Traitement du signal et des images Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] attribut [Termes IGN] compression d'image [Termes IGN] compression de données [Termes IGN] géométrie [Termes IGN] semis de points [Termes IGN] stockage de données Index. décimale : THESE Thèses et HDR Résumé : (auteur) Point clouds are becoming essential in key applications with advances in capture technologies leading to large volumes of data.Compression is thus essential for storage and transmission.Point Cloud Compression can be divided into two parts: geometry and attribute compression.In addition, point cloud quality assessment is necessary in order to evaluate point cloud compression methods.Geometry compression, attribute compression and quality assessment form the three main parts of this dissertation.The common challenge across these three problems is the sparsity and irregularity of point clouds.Indeed, while other modalities such as images lie on a regular grid, point cloud geometry can be considered as a sparse binary signal over 3D space and attributes are defined on the geometry which can be both sparse and irregular.First, the state of the art for geometry and attribute compression methods with a focus on deep learning based approaches is reviewed.The challenges faced when compressing geometry and attributes are considered, with an analysis of the current approaches to address them, their limitations and the relations between deep learning and traditional ones.We present our work on geometry compression: a convolutional lossy geometry compression approach with a study on the key performance factors of such methods and a generative model for lossless geometry compression with a multiscale variant addressing its complexity issues.Then, we present a folding-based approach for attribute compression that learns a mapping from the point cloud to a 2D grid in order to reduce point cloud attribute compression to an image compression problem.Furthermore, we propose a differentiable deep perceptual quality metric that can be used to train lossy point cloud geometry compression networks while being well correlated with perceived visual quality and a convolutional neural network for point cloud quality assessment based on a patch extraction approach.Finally, we conclude the dissertation and discuss open questions in point cloud compression, existing solutions and perspectives. We highlight the link between existing point cloud compression research and research problems to relevant areas of adjacent fields, such as rendering in computer graphics, mesh compression and point cloud quality assessment. Note de contenu : 1- Introduction 2- State of the Art on point cloud compression 3- Convolutional neural networks for lossy PCGC 4- Deep generative model for lossless PCGC 5- Deep multiscale lossless PCGC 6- Folding-based PCAC 7- Deep perceptual point cloud quality metric 8- Convolutional Neural Network for PCQA Numéro de notice : 24081 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de doctorat : Traitement du signal et des images : Paris-Saclay : 2022 Organisme de stage : Laboratoire des signaux et systèmes DOI : sans En ligne : https://theses.hal.science/tel-03894261 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102331

Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression / Ignacio Hernández-Bautista in The Visual Computer, vol 37 n° 5 (May 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 5 (May 2021) . - pp 957 - 972 Titre : Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression Type de document : Article/Communication Auteurs : Ignacio Hernández-Bautista, Auteur ; Jesús Ariel Carrasco-Ochoa, Auteur ; José Francisco Martínez-Trinidad, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 957 - 972 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse spectrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compression d'image [Termes IGN] décomposition spectrale [Termes IGN] transformation en ondelettes Résumé : (auteur) In this paper, a new method for automatic filter coefficient calculation in lifting scheme wavelet transform for image lossless compression is proposed. Actually, there is no specific rule for setting filter coefficients (a, b). Therefore, this work proposes an automatic method to calculate the filter coefficients depending on the spectral analysis of each image. Also, filter coefficients are determined for five decomposition levels and for each quadrant through applying the discrete wavelet transform in the lossless image compression problem. Spectral patterns are computed and fixed into small length vectors for building different wavelet decomposition levels; these vectors are automatically computed using a 1-NN classifier. Experimental results over standard images show that calculating the wavelet filter coefficients using the proposed method generates higher compression rates (in entropy and bitstream values) against standard wavelet and linear prediction filters. Numéro de notice : A2021-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01846-0 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1007/s00371-020-01846-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97693 [article]

3D point cloud compression / Chao Cao (2021)  

Public Titre : 3D point cloud compression Type de document : Thèse/HDR Auteurs : Chao Cao, Auteur ; Titus Zaharia, Directeur de thèse ; Marius Preda, Directeur de thèse Editeur : Paris : Institut Polytechnique de Paris Année de publication : 2021 Importance : 165 p. Format : 21 x 30 cm Note générale : Bibliographie Thèse de doctorat de l’Institut polytechnique de Paris, Spécialité Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] compression d'image [Termes IGN] corrélation automatique de points homologues [Termes IGN] couleur (variable spectrale) [Termes IGN] état de l'art [Termes IGN] objet 3D [Termes IGN] précision géométrique (imagerie) [Termes IGN] scène 3D [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] structure-from-motion Index. décimale : THESE Thèses et HDR Résumé : (Auteur) With the rapid growth of multimedia content, 3D objects are becoming more and more popular. Most of the time, they are modeled as complex polygonal meshes or dense point clouds, providing immersive experiences in different industrial and consumer multimedia applications. The point cloud, which is easier to acquire than mesh and is widely applicable, has raised many interests in both the academic and commercial worlds.A point cloud is a set of points with different properties such as their geometrical locations and the associated attributes (e.g., color, material properties, etc.). The number of the points within a point cloud can range from a thousand, to constitute simple 3D objects, up to billions, to realistically represent complex 3D scenes. Such huge amounts of data bring great technological challenges in terms of transmission, processing, and storage of point clouds.In recent years, numerous research works focused their efforts on the compression of meshes, while less was addressed for point clouds. We have identified two main approaches in the literature: a purely geometric one based on octree decomposition, and a hybrid one based on both geometry and video coding. The first approach can provide accurate 3D geometry information but contains weak temporal consistency. The second one can efficiently remove the temporal redundancy yet a decrease of geometrical precision can be observed after the projection. Thus, the tradeoff between compression efficiency and accurate prediction needs to be optimized.We focused on exploring the temporal correlations between dynamic dense point clouds. We proposed different approaches to improve the compression performance of the MPEG (Moving Picture Experts Group) V-PCC (Video-based Point Cloud Compression) test model, which provides state-of-the-art compression on dynamic dense point clouds.First, an octree-based adaptive segmentation is proposed to cluster the points with different motion amplitudes into 3D cubes. Then, motion estimation is applied to these cubes using affine transformation. Gains in terms of rate-distortion (RD) performance have been observed in sequences with relatively low motion amplitudes. However, the cost of building an octree for the dense point cloud remains expensive while the resulting octree structures contain poor temporal consistency for the sequences with higher motion amplitudes.An anatomical structure is then proposed to model the motion of the point clouds representing humanoids more inherently. With the help of 2D pose estimation tools, the motion is estimated from 14 anatomical segments using affine transformation.Moreover, we propose a novel solution for color prediction and discuss the residual coding from prediction. It is shown that instead of encoding redundant texture information, it is more valuable to code the residuals, which leads to a better RD performance.Although our contributions have improved the performances of the V-PCC test models, the temporal compression of dynamic point clouds remains a highly challenging task. Due to the limitations of the current acquisition technology, the acquired point clouds can be noisy in both geometry and attribute domains, which makes it challenging to achieve accurate motion estimation. In future studies, the technologies used for 3D meshes may be exploited and adapted to provide temporal-consistent connectivity information between dynamic 3D point clouds. Note de contenu : Chapter 1 - Introduction 1.1. Background and motivation 1.2. Outline of the thesis and contributions Chapter 2 - 3D Point Cloud Compression: State of the art 2.1. The 3D PCC “Universe Map” for methods 2.2. 1D methods: geometry traversal 2.3. 2D methods: Projection and mapping onto 2D planar domains 2.4. 3D methods: Direct exploitation of 3D correlations 2.5. DL-based methods 2.6. 3D PCC: What is missing? 2.7. MPEG 3D PCC standards Chapter 3 - Extended Study of MPEG V-PCC and G-PCC Approaches 3.1. V-PCC methodology 3.2. Experimental evaluation of V-PCC 3.3. G-PCC methodology 3.4. Experimental evaluation of G-PCC 3.5. Experiments on the V-PCC inter-coding mode 3.6. Conclusion Chapter 4 - Octree-based RDO segmentation 4.1. Pipeline 4.2. RDO-based octree segmentation 4.3. Prediction modeS 4.4. Experimental results 4.5. Conclusion Chapter 5 - Skeleton-based motion estimation and compensation 5.1. Introduction 5.2. 3D Skeleton Generation 5.3. Motion estimation and compression 5.4. Experimental results 5.5. Conclusion Chapter 6 - Temporal prediction using anatomical segmentation 6.1. Introduction 6.2. A novel dynamic 3D point cloud dataset 6.3. Prediction structure 6.4. Improved anatomy segmentation 6.5. Experimental results 6.6. Conclusion Chapter 7 - A novel color compression for point clouds using affine transformation 7.1. Introduction 7.2. The residuals from both geometry and color 7.3. The prediction structure 7.4. Compression of the color residuals 7.5. Experimental results 7.6. Conclusion Chapter 8 - Conclusion and future work 8.1. Conclusion 8.2. Future work Numéro de notice : 26821 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : informatique : Paris : 2021 Organisme de stage : Telecom SudParis nature-HAL : Thèse DOI : sans Date de publication en ligne : 13/04/2022 En ligne : https://tel.hal.science/tel-03524521 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100476

Satellite systems / Tien M. Nguyen (2021)  

Public Titre : Satellite systems : design, modeling, simulation and analysis Type de document : Monographie Auteurs : Tien M. Nguyen, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 ISBN/ISSN/EAN : 978-1-83968-374-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Technologies spatiales [Termes IGN] compression d'image [Termes IGN] données GNSS [Termes IGN] données spatiotemporelles [Termes IGN] image à haute résolution [Termes IGN] image optique [Termes IGN] microsatellite [Termes IGN] télédétection spatiale [Termes IGN] température de surface de la mer [Termes IGN] véhicule spatial Index. décimale : 21.00 Technologies spatiales Résumé : (Editeur) This book provides a high-level overview of the current state of the art and future of satellite systems, satellite control systems, and satellite systems design. Chapters cover such topics as existing and future satellite systems, satellite communication subsystems, space control and Space Situation Awareness (SAA), machine learning methods with novel neural networks, data measurements in Global Navigation Satellite Systems, and much more. This volume is a practical reference for system engineers, design engineers, system analysts, and researchers in satellite engineering and advanced mathematical modeling fields. Note de contenu : 1. Communication Subsystems for Satellite Design / By Hung H. Nguyen and Peter S. Nguyen 2. Overview of Existing and Future Advanced Satellite Systems / By John Nguyen 3. Game Theoretic Training Enabled Deep Learning Solutions for Rapid Discovery of Satellite Behaviors / By Dan Shen, Carolyn Sheaff, Genshe Chen, Jingyang Lu, Mengqing Guo, Erik Blasch and Khanh Pham 4. Future Satellite System Architectures and Practical Design Issues: An Overview / By Tien M. Nguyen 5. System Designs of Microsatellites: A Review of Two Schools of Thoughts / By Triharjanto Robertus 6. Design of Intelligent and Open Avionics System Onboard / By Changqing Wu, Xiaodong Han and Yakun Wang 7. Dynamic Link from Liftoff to Final Orbital Insertion for a MEO Space Vehicle / By Jack K. Kreng and Gleason Q. Chen 8. Effective Algorithms for Detection Outliers and Cycle Slip Repair in GNSS Data Measurements / By Igor V. Bezmenov 9. Analysis of Spatiotemporal Variability of Surface Temperature of Okhotsk Sea and Adjacent Waters Using Satellite Data / By Dmitry Lozhkin 10. Compression of High-Resolution Satellite Images Using Optical Image Processing / By Anirban Patra, Arijit Saha, Debasish Chakraborty and Kallol Bhattacharya Numéro de notice : 26712 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.73789 Date de publication en ligne : 14/04/2021 En ligne : https://doi.org/10.5772/intechopen.73789 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99491

Learning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)  

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Convolutional Neural Networks for embedded vision / Lucas Fernandez Brillet (2020)  

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Fractal analysis / Robert Koprowski (2020)  

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Sparse signal modeling: Application to image compression, Image error concealment and compressed sensing / Ali Akbari (2018)  

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Statistical atmospheric parameter retrieval largely benefits from spatial–spectral image compression / Joaquín García-Sobrino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)  

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Adaptive spectral–spatial compression of hyperspectral image with sparse representation / Wei Fu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 2 (February 2017)  

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Quality assessment methodologies of post-processed images / Lukas Krasula (2017)  

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Retina-inspired image and video coding / Effrosyni Doutsi (2017)  

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Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing / Lei Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)  

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Compressive sensing for multibaseline polarimetric SAR tomography of forested areas / Xinwu Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

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