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Learning-based hyperspectral imagery compression through generative neural networks / Chubo Deng in Remote sensing, vol 12 n° 21 (November 2020)
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Titre : Learning-based hyperspectral imagery compression through generative neural networks Type de document : Article/Communication Auteurs : Chubo Deng, Auteur ; Yi Cen, Auteur ; Lifu Zhang, Auteur Année de publication : 2020 Article en page(s) : n° 3657 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] apprentissage profond
[Termes IGN] compression d'image
[Termes IGN] compression par ondelettes
[Termes IGN] image hyperspectrale
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Hyperspectral images (HSIs), which obtain abundant spectral information for narrow spectral bands (no wider than 10 nm), have greatly improved our ability to qualitatively and quantitatively sense the Earth. Since HSIs are collected by high-resolution instruments over a very large number of wavelengths, the data generated by such sensors is enormous, and the amount of data continues to grow, HSI compression technique will play more crucial role in this trend. The classical method for HSI compression is through compression and reconstruction methods such as three-dimensional wavelet-based techniques or the principle component analysis (PCA) transform. In this paper, we provide an alternative approach for HSI compression via a generative neural network (GNN), which learns the probability distribution of the real data from a random latent code. This is achieved by defining a family of densities and finding the one minimizing the distance between this family and the real data distribution. Then, the well-trained neural network is a representation of the HSI, and the compression ratio is determined by the complexity of the GNN. Moreover, the latent code can be encrypted by embedding a digit with a random distribution, which makes the code confidential. Experimental examples are presented to demonstrate the potential of the GNN to solve image compression problems in the field of HSI. Compared with other algorithms, it has better performance at high compression ratio, and there is still much room left for improvements along with the fast development of deep-learning techniques. Numéro de notice : A2020-720 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12213657 Date de publication en ligne : 08/11/2020 En ligne : https://doi.org/10.3390/rs12213657 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96310
in Remote sensing > vol 12 n° 21 (November 2020) . - n° 3657[article]
Titre : A computational introduction to digital image processing Type de document : Monographie Auteurs : Alasdair McAndrew Mention d'édition : Second edition Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2016 Importance : 535 p. Présentation : illustrations Format : 18 x 26 cm ISBN/ISSN/EAN : 978-1-4822-4732-9 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compression par ondelettes
[Termes IGN] GNU Octave
[Termes IGN] image en couleur
[Termes IGN] Matlab
[Termes IGN] Python (langage de programmation)
[Termes IGN] restauration d'image
[Termes IGN] segmentation d'image
[Termes IGN] transformation de Fourier
[Termes IGN] voisinage (relation topologique)Index. décimale : 35.20 Traitement d'image Résumé : (Editeur) This book explores the nature and use of digital images and shows how they can be obtained, stored, and displayed. Taking a strictly elementary perspective, the book only covers topics that involve simple mathematics yet offer a very broad and deep introduction to the discipline. This second edition provides users with three different computing options. Along with MATLAB®, this edition now includes GNU Octave and Python. Users can choose the best software to fit their needs or migrate from one system to another. Programs are written as modular as possible, allowing for greater flexibility, code reuse, and conciseness. This edition also contains new images, redrawn diagrams, and new discussions of edge-preserving blurring filters, ISODATA thresholding, Radon transform, corner detection, retinex algorithm, LZW compression, and other topics. Based on the author’s successful image processing courses, this bestseller is suitable for classroom use or self-study. In a straightforward way, the text illustrates how to implement imaging techniques in MATLAB, GNU Octave, and Python. It includes numerous examples and exercises to give students hands-on practice with the material. Note de contenu :
1. Introduction
2. Images Files and File Types
3. Image Display
4. Point Processing
5. Neighborhood Processing
6. Image Geometry
7. The Fourier Transform
8. Image Restoration
9. Image Segmentation
10. Mathematical Morphology
11. Image Topology
12. Shapes and Boundaries
13. Color Processing
14. Image Coding and Compression
15. Wavelets
16. Special Effects
Appendix A: Introduction to MATLAB and Octave
Appendix B: Introduction to Python
Appendix C: The Fast Fourier TransformNuméro de notice : 22951 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Monographie Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91638 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22951-01 35.20 Livre Centre de documentation Télédétection Disponible Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas / Esteban Aguilera in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)
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Titre : Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas Type de document : Article/Communication Auteurs : Esteban Aguilera, Auteur ; Matteo Nannini, Auteur ; Andreas Reigber, Auteur Année de publication : 2013 Article en page(s) : pp 5283 - 5295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Allemagne
[Termes IGN] bande L
[Termes IGN] compression par ondelettes
[Termes IGN] données polarimétriques
[Termes IGN] forêt
[Termes IGN] image E-SAR
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] polarimétrie radar
[Termes IGN] tomographie radarRésumé : (Auteur) Synthetic aperture radar (SAR) tomography is a 3-D imaging modality that is commonly tackled by spectral estimation techniques. Thus, the backscattered power along the cross-range direction can be readily obtained by computing the Fourier spectrum of a stack of multibaseline measurements. In addition, recent work has addressed the tomographic inversion under the framework of compressed sensing, thereby recovering sparse cross-range profiles from a reduced set of measurements. This paper differs from previous publications, in that it focuses on sparse expansions in the wavelet domain while working with the second-order statistics of the corresponding multibaseline measurements. In this regard, we elaborate on the conditions under which this perspective is applicable to forested areas and discuss the possibility of optimizing the acquisition geometry. Finally, we compare this approach with traditional nonparametric ones and validate it by using fully polarimetric L-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR). Numéro de notice : A2013-696 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2231081 En ligne : https://doi.org/10.1109/TGRS.2012.2231081 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32832
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 12 (December 2013) . - pp 5283 - 5295[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013121 RAB Revue Centre de documentation En réserve L003 Disponible Effect of Jpeg2000 on the information and geometry content of aerial photo compression / J.K. Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 2 (February 2005)
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Titre : Effect of Jpeg2000 on the information and geometry content of aerial photo compression Type de document : Article/Communication Auteurs : J.K. Liu, Auteur ; H.C. Wu, Auteur ; T.Y. Shih, Auteur Année de publication : 2005 Article en page(s) : pp 157 - 167 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] compression d'image
[Termes IGN] compression par ondelettes
[Termes IGN] contrôle qualité
[Termes IGN] erreur moyenne quadratique
[Termes IGN] format JPEG
[Termes IGN] image aérienne
[Termes IGN] point de repère
[Termes IGN] qualité des données
[Termes IGN] qualité géométrique (image)
[Termes IGN] qualité radiométrique (image)
[Termes IGN] rapport signal sur bruit
[Termes IGN] transformation en cosinus discrèteRésumé : (Auteur) The standardization effort of the next ISO standard for compression of the still image, JPEG2000, has recently reached International Standard (IS) status. This wavelet-based standard outperforms the Discrete Cosine Transform (DCT) based JPEG in terms of compression ratio, as well as, quality. In this study, the performance of JPEG2000 is evaluated for aerial image compressions. Different compression ratios are applied to scanned aerial photos at the 1:5 000 scale. Both the image quality measurements and the accuracy of photogrammetric point determination aspects are examined. The evaluation of image quality is based on visual analysis of the objects in the scene and on the computation of numerical indices, including RMSE, entropy, and Peak Signal-to-Noise Ratio (PSNR). The geometric quality of JPEG2000 with different compression ratios is studied for some photogrammetric operations, including interior orientation, relative orientation, absolute orientation, and DSM generation. The objective of this study is to explore the possibility of JPEG2000 for replacing JPEG as a standard in photogrammetric operations. Numéro de notice : A2005-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.71.2.157 En ligne : https://doi.org/10.14358/PERS.71.2.157 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27167
in Photogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 2 (February 2005) . - pp 157 - 167[article]
Titre : Wavelettransformation hybrider Geländemodelle Titre original : [La transformation en ondelettes des modèles hybrides de terrain] Type de document : Thèse/HDR Auteurs : G. Beyer, Auteur Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2005 Collection : DGK - C Sous-collection : Dissertationen num. 570 Importance : 116 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-3-7696-5009-9 Langues : Allemand (ger) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] compression de données
[Termes IGN] compression par ondelettes
[Termes IGN] modèle numérique de terrain
[Termes IGN] transformation en ondelettesIndex. décimale : 33.30 Photogrammétrie numérique Note de contenu : 1 Einleitung
2 WaveletTransformation zur Analyse, Approximation und Kompression
2.1 Integraltransformationen
2.2 Kontinuierliche Wavelettransformation
3 Diskrete Wavelettransformation
3.1 Matrizenbeziehungen diskreter Funktionen
3.2 Konzept der Multiskalenanalyse
3.3 Konstruktion der diskreten Wavelets
3.3.1 Konstruktion der eindimensionalen Wavelets
3.3.2 Konstruktion der zweidimensionalen Wavelets
3.4 Verfahren der diskreten Wavelettransformation
3.4.1 Verfahren der 1DWavelettransformation
3.4.2 Verfahren der 2DWavelettransformation
3.5 Wavelettransformation einer zweidimensionalen Polynomfunktion
3.5.1 Wavelettransforination einer Funktion z = xy
3.5.2 Wavelettransformation einer Polynomfunktion z = P(x, y)
3.6 Momente
4 Digitale Geländemodelle
4.1 Modelle für Kurven und Flächen
4.2 Neigung und Krümmung
4.3 Raumkurven in Geländemodellen
4.3.1 Explizite Beschreibung
4.3.2 Implizite Beschreibung
5 Wavelettransformation digitaler Geländemodelle
5.1 Wavelettransformation der Geländefläche
5.2 Wavelettransformation von Kurven in expliziter Darstellung
5.3 Wavelettransformation von Kurven in impliziter Darstellung
6 Kompression von Geländedaten
6.1Vorbemerkungen
6.2 Kompressionsverfahren
6.2.1 Sperrfilter
6.2.2 Schwellwertverfahren
6.3 Kompression von Geländeflächen
6.1 Kompression von Geländekurven
6.5 Probleme bei hybriden Daten
6.5.1 Gemeinsame Kompression von Vektor- und Rasterdaten
6.5.2 Glättungsmaße
6.5.3 Kantenanpassung
7 Analyse von Geländemodellen mittels Wavelettransformation (Folgeprodukte)
7.1 Approximationseigenschaften
7.1.1 Theoretischer Hintergrund
7.1.2 Approximationseigenschaften bei der diskreten Wavelettransformation
7.1.3 Skalierung der Waveletkoeffizienten
7.2 Lokalisierungseigenschaften
7.2.1 Eindirnensionale Wavelettransforrnation
7.2.2 Zweidimensionale Wavelettransformation
7.3 Höhen- und Gefällefinien
8 Ausblick
8.1 Wavelettransformation raumbezogener Geoinformationen
8.2 Weitere FragestellungenNuméro de notice : 13255 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=54939 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 13255-01 33.30 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible PermalinkPermalinkA new wavelet-based tool for combined image processing and compression of remote sensing data / B. Triebfurst in GIS Geo-Informations-Systeme, vol 13 n° 2 (April 2000)
PermalinkECW: wavelet compression beyond limits? [Enhanced Compressed Wavelet] / J. Triglav in Geoinformatics, vol 3 n° 1 (01/01/2000)
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