Descripteur
Documents disponibles dans cette catégorie (60)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Band-limited signal reconstruction from irregular samples with variable apertures / David G. Long in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)
[article]
Titre : Band-limited signal reconstruction from irregular samples with variable apertures Type de document : Article/Communication Auteurs : David G. Long, Auteur ; Reinhard O. W. Franz, Auteur Année de publication : 2016 Article en page(s) : pp 2424 - 2436 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] échantillonnage de signal
[Termes IGN] largeur de bande
[Termes IGN] reconstruction d'image
[Termes IGN] reconstruction du signal
[Termes IGN] télédétectionRésumé : (Auteur) Sampling plays a critical role in remote sensing and signal analysis. In conventional sampling theory, the signal is sampled at a uniform rate at a minimum of twice the signal bandwidth. Sampling with an aperture function requires a fixed-aperture function, which can be removed by deconvolution after signal reconstruction. However, in some cases, the signal samples are available only at irregular positions, and different samples use different aperture functions. In this paper, the theory of finite-length signal reconstruction with irregular samples and variable apertures in one and two dimensions is considered. In the 1-D case, a band-limited discrete signal can be exactly reconstructed from a finite number of arbitrarily spaced samples with few restrictions on the aperture functions. Exact reconstruction in the 2-D case requires the sampling matrix be invertable, and is not always possible. Variable aperture functions, while complicating the process, can enable reconstruction for a broader range of sample locations. Practical issues are discussed, and numerical examples are provided. Variable aperture reconstruction has application in a variety of remote sensing problems. In this paper, reconstruction from 2-D irregular sampling with variable apertures is illustrated using Special Sensor Microwave/Imager radiometer observations. Numéro de notice : A2016-842 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2501366 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2501366 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82886
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 2424 - 2436[article]Thin cloud removal based on signal transmission principles and spectral mixture analysis / Meng Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)
[article]
Titre : Thin cloud removal based on signal transmission principles and spectral mixture analysis Type de document : Article/Communication Auteurs : Meng Xu, Auteur ; Mark Pickering, Auteur ; Antonio J. Plaza, Auteur ; Xiuping Jia, Auteur Année de publication : 2016 Article en page(s) : pp 1659 - 1669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification pixellaire
[Termes IGN] correction d'image
[Termes IGN] épaisseur de nuage
[Termes IGN] nuage
[Termes IGN] rayonnement solaire
[Termes IGN] reconstruction d'imageRésumé : (Auteur) Cloud removal is an important goal for enhancing the utilization of optical remote sensing satellite images. Clouds dynamically affect the signal transmission due to their different shapes, heights, and distribution. In the case of thick opaque clouds, pixel replacement has been commonly adopted. For thin clouds, pixel correction techniques allow the effects of thin clouds to be removed while retaining the remaining information in the contaminated pixels. In this paper, we develop a new method based on signal transmission and spectral mixture analysis for pixel correction which makes use of a cloud removal model that considers not only the additive reflectance from the clouds but also the energy absorption when solar radiation passes through them. Data correction is achieved by subtracting the product of the cloud endmember signature and the cloud abundance and rescaling according to the cloud thickness. The proposed method has no requirement for meteorological data and does not rely on reference images. Our experimental results indicate that the proposed approach is able to perform effective removal of thin clouds in different scenarios. Numéro de notice : A2016-125 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2486780 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2486780 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80006
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 3 (March 2016) . - pp 1659 - 1669[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016031 SL Revue Centre de documentation Revues en salle Disponible Airborne DLSLA 3-D SAR image reconstruction by combination of polar formatting and L_1 regularization / Xueming Peng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)
[article]
Titre : Airborne DLSLA 3-D SAR image reconstruction by combination of polar formatting and L_1 regularization Type de document : Article/Communication Auteurs : Xueming Peng, Auteur ; Weixian Tan, Auteur ; Wen Hong, Auteur Année de publication : 2016 Article en page(s) : pp 213 - 226 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] bande X
[Termes IGN] capteur aérien
[Termes IGN] centre de phase
[Termes IGN] image radar moirée
[Termes IGN] polarisation
[Termes IGN] reconstruction d'imageRésumé : (Auteur) Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) operates downward-looking observation and obtains the 3-D microwave scattering information of the observed scene. The cross-track physical sparse linear array is often configured to obtain uniform virtual phase centers in order to adopt the frequency-domain algorithm. However, the virtual phase centers usually have to be nonuniformly and sparsely distributed due to the array elements' installation locations restricted by the airborne platform and the airborne wing tremor effect. In this state, the frequency-domain algorithm cannot be directly used. In this paper, a DLSLA 3-D SAR image reconstruction algorithm that combines polar formatting and L1 regularization is presented. Wave propagation and along-track dimensional imaging are first finished after polar formatting and wavefront curvature phase error compensation; then, cross-track dimensional imaging is completed with the L1 regularization technique. The proposed algorithm is applicable to airborne DLSLA 3-D SAR imaging under nonuniformly and sparsely distributed virtual phase centers condition. The proposed algorithm was verified by 3-D distributed scene simulation experiment (P-band circular SAR image was selected as radar cross-section input, and X-band digital elevation model of the same area was selected as the coordinate positions of the scene) and the field experiment. Image reconstruction results and image reconstruction performances, such as normalized radar cross section, height errors, and orthographic projection image grayscale distribution, are demonstrated and analyzed with different signal-to-noise ratios, different array sparsity, and the incomplete compensated residual oscillation error 3-D distributed scene simulation experiments. Simulation and field experimental results show the good performance in focusing and the robustness of the proposed algorithm. Numéro de notice : A2016-077 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2453202 En ligne : https://doi.org/10.1109/TGRS.2015.2453202 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79846
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 213 - 226[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016011 SL Revue Centre de documentation Revues en salle Disponible HYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
[article]
Titre : HYCA: A new technique for hyperspectral compressive sensing Type de document : Article/Communication Auteurs : G. Martin, Auteur ; José M. Bioucas-Dias, Auteur ; Antonio J. Plaza, Auteur Année de publication : 2015 Article en page(s) : pp 2819 - 2831 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] capteur hyperspectral
[Termes IGN] compression d'image
[Termes IGN] coordonnées géographiques
[Termes IGN] corrélation
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'imageRésumé : (Auteur) Hyperspectral imaging relies on sophisticated acquisition and data processing systems able to acquire, process, store, and transmit hundreds or thousands of image bands from a given area of interest. In this paper, we exploit the high correlation existing among the components of the hyperspectral data sets to introduce a new compressive sensing methodology, termed hyperspectral coded aperture (HYCA), which largely reduces the number of measurements necessary to correctly reconstruct the original data. HYCA relies on two central properties of most hyperspectral images, usually termed data cubes: 1) the spectral vectors live on a low-dimensional subspace; and 2) the spectral bands present high correlation in both the spatial and the spectral domain. The former property allows to represent the data vectors using a small number of coordinates. In this paper, we particularly exploit the high spatial correlation mentioned in the latter property, which implies that each coordinate is piecewise smooth and thus compressible using local differences. The measurement matrix computes a small number of random projections for every spectral vector, which is connected with coded aperture schemes. The reconstruction of the data cube is obtained by solving a convex optimization problem containing a data term linked to the measurement matrix and a total variation regularizer. The solution of this optimization problem is obtained by an instance of the alternating direction method of multipliers that decomposes very hard problems into a cyclic sequence of simpler problems. In order to address the need to set up the parameters involved in the HYCA algorithm, we also develop a constrained version of HYCA (C-HYCA), in which all the parameters can be automatically estimated, which is an important aspect for practical application of the algorithm. A series of experiments with simulated and real data shows the effectiveness of HYCA and C-HYCA, indicating their potential in real-world applications. Numéro de notice : A2015-520 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2365534 En ligne : https://doi.org/10.1109/TGRS.2014.2365534 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77527
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2819 - 2831[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Signal Processing : A Mathematical Approach Type de document : Monographie Auteurs : Charles L. Byrne, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2014 Importance : 397 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-0-429-15871-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] détection du signal
[Termes IGN] écho radar
[Termes IGN] filtre de Wiener
[Termes IGN] phase
[Termes IGN] probabilités
[Termes IGN] propagation du signal
[Termes IGN] reconstruction d'image
[Termes IGN] série de Fourier
[Termes IGN] signal acoustique
[Termes IGN] tomographie
[Termes IGN] transformation de Fourier
[Termes IGN] transformation en ondelettesRésumé : (éditeur) A practical guide to the mathematics behind signal processing, this book provides the essential mathematical background and tools necessary to understand and employ signal processing techniques. Topics addressed include: - Fourier series and transforms in one and several variables, - applications to acoustic and electromagnetic propagation models, - transmission and emission tomography and image reconstruction, - optimization techniques, - high-resolution methods, and more. The emphasis is on the general problem of extracting information from limited data obtained by some form of remote sensing: acoustic or radar processing, satellite imaging, or medical tomographic scanning. Note de contenu : 1- Introduction
2- Fourier Series and Fourier Transforms
3- Remote Sensing
4- Finite-Parameter Models
5- Transmission and Remote Sensing
6- The Fourier Transform and Convolution Filtering
7- Infinite Sequences and Discrete Filters
8- Convolution and the Vector DFT
9- Plane-Wave Propagation
10- The Phase Problem
11- Transmission Tomography
12- Random Sequences
13- Nonlinear Methods
14- Discrete Entropy Maximization
15- Analysis and Synthesis
16- Wavelets
17- The BLUE and the Kalman Filter
18- Signal Detection and Estimation
19- Inner Products
20- Wiener Filtering
21- Matrix Theory
22- Compressed Sensing
23- Probability
24- Using the Wave Equation
25- Reconstruction in Hilbert Space
26- Some Theory of Fourier Analysis
27- Reverberation and Echo CancellationNuméro de notice : 25846 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Monographie En ligne : https://www.taylorfrancis.com/books/9780429158711 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95260 Integrated denoising and unwrapping of INSAR phase based on Markov random fields / Runpu Chen in IEEE Transactions on geoscience and remote sensing, vol 51 n° 8 (August 2013)PermalinkAttraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery / Xiaohua Tong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkClassification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkJoint wall mitigation and compressive sensing for indoor image reconstruction / E. Lagunas in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkThrough-the-wall human motion indication using sparsity-driven change detection / F. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkA complete processing chain for shadow detection and reconstruction in VHR images / L. Lorenzi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)PermalinkRecovering quasi-real occlusion-free textures for facade models by exploiting fusion of image and laser street data and image inpainting / Karim Hammoudi (2012)PermalinkSuperresolution enhancement of hyperspectral CHRIS/Proba images with a thin-plate spline nonrigid transform model / J. Chan in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkProblèmes inverses en imagerie et en vision, 2. Volume 2 / A. Mohammad-Djafari (2009)PermalinkContextual reconstruction of cloud-contaminated multitemporal multispectral image / F. Melgani in IEEE Transactions on geoscience and remote sensing, vol 44 n° 2 (February 2006)Permalink