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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)
[article]
Titre : Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing Type de document : Article/Communication Auteurs : Lei Zhang, Auteur ; Wei Wei, Auteur ; Yanning Zhang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 7223 - 7235 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] bruit blanc
[Termes IGN] compression d'image
[Termes IGN] image hyperspectrale
[Termes IGN] reconstruction d'imageRésumé : (Auteur) The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models. With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved. Numéro de notice : A2016-929 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2598577 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2598577 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83343
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 12 (December 2016) . - pp 7223 - 7235[article]On the spectral combination of satellite gravity model, terrestrial and airborne gravity data for local gravimetric geoid computation / Tao Jian in Journal of geodesy, vol 90 n° 12 (December 2016)
[article]
Titre : On the spectral combination of satellite gravity model, terrestrial and airborne gravity data for local gravimetric geoid computation Type de document : Article/Communication Auteurs : Tao Jian, Auteur ; Yan Ming Wang, Auteur Année de publication : 2016 Article en page(s) : pp 1405 - 1418 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique
[Termes IGN] analyse harmonique
[Termes IGN] bruit blanc
[Termes IGN] champ de pesanteur terrestre
[Termes IGN] erreur
[Termes IGN] géoïde gravimétrique
[Termes IGN] géoïde local
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] varianceRésumé : (Auteur) One of the challenges for geoid determination is the combination of heterogeneous gravity data. Because of the distinctive spectral content of different data sets, spectral combination is a suitable candidate for its solution. The key to have a successful combination is to determine the proper spectral weights, or the error degree variances of each data set. In this paper, the error degree variances of terrestrial and airborne gravity data at low degrees are estimated by the aid of a satellite gravity model using harmonic analysis. For higher degrees, the error covariances are estimated from local gravity data first, and then used to compute the error degree variances. The white and colored noise models are also used to estimate the error degree variances of local gravity data for comparisons. Based on the error degree variances, the spectral weights of satellite gravity models, terrestrial and airborne gravity data are determined and applied for geoid computation in Texas area. The computed gravimetric geoid models are tested against an independent, highly accurate geoid profile of the Geoid Slope Validation Survey 2011 (GSVS11). The geoid computed by combining satellite gravity model GOCO03S and terrestrial (land and DTU13 altimetric) gravity data agrees with GSVS11 to ±1.1 cm in terms of standard deviation along a line of 325 km. After incorporating the airborne gravity data collected at 11 km altitude, the standard deviation is reduced to ±0.8 cm. Numerical tests demonstrate the feasibility of spectral combination in geoid computation and the contribution of airborne gravity in an area of high quality terrestrial gravity data. Using the GSVS11 data and the spectral combination, the degree of correctness of the error spectra and the quality of satellite gravity models can also be revealed. Numéro de notice : A2016-810 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-016-0932-7 En ligne : http://dx.doi.org/10.1007/s00190-016-0932-7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82602
in Journal of geodesy > vol 90 n° 12 (December 2016) . - pp 1405 - 1418[article]On the significance of periodic signals in noise analysis of GPS station coordinates time series / Janusz Bogusz in GPS solutions, vol 20 n° 4 (October 2016)
[article]
Titre : On the significance of periodic signals in noise analysis of GPS station coordinates time series Type de document : Article/Communication Auteurs : Janusz Bogusz, Auteur ; Anna Klos, Auteur Année de publication : 2016 Article en page(s) : pp 655 - 664 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] analyse diachronique
[Termes IGN] bruit (théorie du signal)
[Termes IGN] coordonnées cartésiennes géocentriques
[Termes IGN] GIPSY-OASIS
[Termes IGN] positionnement ponctuel précis
[Termes IGN] série temporelle
[Termes IGN] signal GPS
[Termes IGN] station GPS
[Termes IGN] variation saisonnière
[Termes IGN] vitesseRésumé : (Auteur) Each of the GPS-derived time series consists of the deterministic (functional) and stochastic part. We propose that the deterministic part includes all periodicities from 1st to 9th harmonics of residual Chandler, tropical and draconitic periods and compare it with commonly used calculations of the annual and semi-annual tropical curve. Then, we address the issues of whether all residual periodicities, as proposed here, need to be taken into consideration when performing noise analysis. We use the position time series from 180 International GNSS Service stations obtained at the Jet Propulsion Laboratory using the GIPSY-OASIS software in a Precise Point Positioning mode. The longest series has 22.1 years of GPS daily solutions. The spectral indices range from –0.12 to –0.92, while the median values of “global” spectral indices are equal to: –0.41 ± 0.15, –0.38 ± 0.12 and –0.33 ± 0.18 for North, East and Up components, respectively. All non-modelled geophysical processes or non-included artificial effects in time series lead to an underestimation of errors of velocities, but also to changes in the velocity values themselves. The proposed assumption of seasonals subtraction caused the Akaike information criterion values to show a decrease in the median value of 30 %, which in fact means that all the seasonals mentioned here must be taken into account when analyzing noises. Finally, we noticed that there are some of the GPS stations that improved their velocity uncertainty even of 56 %. Numéro de notice : A2016--027 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s10291-015-0478-9 En ligne : http://dx.doi.org/10.1007/s10291-015-0478-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83929
in GPS solutions > vol 20 n° 4 (October 2016) . - pp 655 - 664[article]Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation / Jie Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)
[article]
Titre : Noise removal from hyperspectral image with joint spectral–spatial distributed sparse representation Type de document : Article/Communication Auteurs : Jie Li, Auteur ; Qiangqiang Yuan, Auteur ; Huanfeng Shen, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 5425 - 5439 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] bruit (théorie du signal)
[Termes IGN] données clairsemées
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] représentation parcimonieuseRésumé : (Auteur) Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral-spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral-spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods. Numéro de notice : A2016-902 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2564639 En ligne : https://doi.org/10.1109/TGRS.2016.2564639 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83095
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5425 - 5439[article]An adaptive stochastic model for GPS observations and its performance in precise point positioning / J. Z. Zheng in Survey review, vol 48 n° 349 (July 2016)
[article]
Titre : An adaptive stochastic model for GPS observations and its performance in precise point positioning Type de document : Article/Communication Auteurs : J. Z. Zheng, Auteur ; F. Guo, Auteur Année de publication : 2016 Article en page(s) : pp 296 - 302 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] bruit (théorie du signal)
[Termes IGN] correction ionosphérique
[Termes IGN] fenêtre (informatique)
[Termes IGN] mesurage de pseudo-distance
[Termes IGN] modèle stochastique
[Termes IGN] positionnement par GPS
[Termes IGN] récepteur GPS
[Termes IGN] temps réelRésumé : (auteur) In this paper, the stochastic characteristics of Global Positioning System (GPS) pseudo-range noise as influenced by several factors such as receiver type, frequency, and ionosphere environment are analysed. The results indicate that the noise level of GPS observations is significantly affected by these factors. Moreover, the noise level is so mutable that it cannot be generalised and described by a uniform empirical model. Even for the same satellite, the noise level of observations may fluctuate sharply in both spatial and temporal resolution. To establish a reasonable stochastic model, a recursive sliding window method for estimating pseudo-range noise in real time is introduced. The effectiveness of the proposed method is verified by specific computational examples. Numéro de notice : A2016-626 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1179/1752270615Y.0000000033 En ligne : https://doi.org/10.1179/1752270615Y.0000000033 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81842
in Survey review > vol 48 n° 349 (July 2016) . - pp 296 - 302[article]Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkTowards reliable velocities of permanent GNSS stations / Janusz Bogusz in Reports on geodesy and geoinformatics, vol 100 (May 2016)PermalinkChange detection between SAR images using a pointwise approach and graph theory / Minh-Tan Pham in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)PermalinkTemporal MODIS data for identification of wheat crop using noise clustering soft classification approach / Priyadarshi Upadhyay in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkCaractérisation des signaux et des bruits des séries temporelles du géocentre et des paramètres de rotation de la Terre (EOP) / Bachir Gourine in Bulletin des sciences géographiques, n° 30 (2015 - 2016)PermalinkChanges in thermal infrared spectra of plants caused by temperature and water stress / Maria F. Buitrago in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)PermalinkPermalinkSpectral–spatial adaptive sparse representation for hyperspectral image denoising / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPermalinkVers la prise en compte de la dépendance spatio temporelle des séries de position GNSS dans leur analyse / Clément Benoist (2016)PermalinkAn approach to fine coregistration between very high resolution multispectral images based on registration noise distribution / Youkyung Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkOn diverse noises in hyperspectral unmixing / Chunzhi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkReduction of PPP convergence period through pseudorange multipath and noise mitigation / Garrett Seepersad in GPS solutions, vol 19 n° 3 (July 2015)PermalinkAssessment of high-rate GPS using a single-axis shake table / Simon Häberling in Journal of geodesy, vol 89 n° 7 (July 2015)PermalinkNetwork-based estimation of time-dependent noise in GPS position time series / Ksenia Dimitrieva in Journal of geodesy, vol 89 n° 6 (June 2015)PermalinkIrregular variations in GPS time series by probability and noise analysis / Anna Klos in Survey review, vol 47 n° 342 (May 2015)PermalinkEssential Earth imaging for GIS / Lawrence Fox III (2015)PermalinkEvaluation des performances de capteurs topographiques pour la mesure de déformation / Julien Assémat (2015)PermalinkDu photon au pixel / Henri Maître (2015)PermalinkNoisy data smoothing in DEM construction using least squares support vector machines / C. Chen in Transactions in GIS, vol 18 n° 6 (December 2014)Permalink