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Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data / C.C. Chew in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2755 - 2764 Titre : Vegetation sensing using GPS-interferometric reflectometry: theoretical effects of canopy parameters on signal-to-noise ratio data Type de document : Article/Communication Auteurs : C.C. Chew, Auteur ; E.E. Small, Auteur ; K. Larson, Auteur ; V. Zavorotny, Auteur Année de publication : 2015 Article en page(s) : pp 2755 - 2764 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] couvert végétal [Termes IGN] humidité du sol [Termes IGN] rapport signal sur bruit [Termes IGN] réflectométrie par GNSS [Termes IGN] signal GPS [Termes IGN] végétation Résumé : (Auteur) The potential to use GPS signal-to-noise ratio (SNR) data to estimate changes in vegetation surrounding a ground-based antenna is evaluated. A 1-D plane-stratified model that simulates the response of GPS SNR data to changes in both soil moisture and vegetation is presented. The model is validated against observations of SNR data from four field sites with varying vegetation cover. Validation shows that the average correlation between modeled and observed SNR data is higher than the average correlation between concurrent SNR observations from different satellite tracks at a site. The model also reproduces variations in the SNR metrics amplitude, phase, and effective reflector height over a range of vegetation wet weights from 0 to 4 kg · m-2, with r2 values of 0.79, 0.84, and 0.62, respectively. Model simulations indicate that the amplitude of SNR oscillations may be used to estimate vegetation amount when vegetation wet weight is below 1.5 kg · m-2. When vegetation wet weight exceeds 1.5 kg · m-2, the sensitivity of amplitude to changes in vegetation amount decreases. Phase of SNR oscillations also varies consistently with vegetation up to 1.5 kg · m-2. However, phase is also very sensitive to soil moisture variations, thus limiting its utility for estimating vegetation. Effective reflector height is not a consistent indicator of vegetation change. Beyond 1.5 kg · m-2, the constant frequency assumption used to characterize SNR fluctuations does not adequately describe observed data. A more complex approach than the standard SNR metrics used here is required to extend GPS-Interferometric Reflectometry sensing to thicker canopies. Numéro de notice : A2015-517 Affiliation des auteurs : non IGN Thématique : FORET/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2364513 En ligne : https://doi.org/10.1109/TGRS.2014.2364513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77523 [article]

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Hyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2467 - 2480 Titre : Hyperspectral image classification based on three-dimensional scattering wavelet transform Type de document : Article/Communication Auteurs : Yuan Yan Tang, Auteur ; Y. Lu, Auteur ; Haoliang Yuan, Auteur Année de publication : 2015 Article en page(s) : pp 2467 - 2480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification spectrale [Termes IGN] diffusion spatiale [Termes IGN] filtrage numérique d'image [Termes IGN] image hyperspectrale [Termes IGN] transformation en ondelettes Résumé : (Auteur) Recent research has shown that utilizing the spectral-spatial information can improve the performance of hyperspectral image (HSI) classification. Since HSI is a 3-D cube datum, 3-D spatial filtering becomes a simple and effective method for extracting the spectral-spatial information. In this paper, we propose a 3-D scattering wavelet transform, which filters the HSI cube data with a cascade of wavelet decompositions, complex modulus, and local weighted averaging. The scattering feature can adequately capture the spectral-spatial information for classification. In the classification step, a support vector machine based on Gaussian kernel is used as a classifier due to its capability to deal with high-dimensional data. Our method is fully evaluated on four classic HSIs, i.e., Indian Pines, Pavia University, Botswana, and Kennedy Space Center. The classification results show that our method achieves as high as 94.46%, 99.30%, 97.57%, and 95.20% accuracies, respectively, when only 5% of the total samples per class is labeled. Numéro de notice : A2015-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2360672 En ligne : https://doi.org/10.1109/TGRS.2014.2360672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77524 [article]

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Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2532 - 2546 Titre : Spectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields Type de document : Article/Communication Auteurs : Junshi Xia, Auteur ; Jocelyn Chanussot, Auteur ; Peijun Du, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2532 - 2546 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse en composantes principales [Termes IGN] champ aléatoire de Markov [Termes IGN] classification et arbre de régression [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] performance [Termes IGN] Rotation Forest classification Résumé : (Auteur) In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification. Numéro de notice : A2015-519 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361618 En ligne : https://doi.org/10.1109/TGRS.2014.2361618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77526 [article]

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HYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2819 - 2831 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'image Résumé : (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 [article]

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Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2899 - 2912 Titre : Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Immaculada Dopido, Auteur ; Paolo Gamba, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2899 - 2912 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification dirigée [Termes IGN] image hyperspectrale Résumé : (Auteur) Classification and spectral unmixing are two important techniques for hyperspectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperspectral image classification that naturally integrates the information provided by discriminative classification and spectral unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by spectral unmixing. In this case, we use a traditional spectral unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperspectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and spectral unmixing in adaptive fashion, offers an effective solution to improve- classification performance in hyperspectral scenes containing mixed pixels. Numéro de notice : A2015-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2366513 En ligne : https://doi.org/10.1109/TGRS.2014.2366513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77532 [article]

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A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2409 - 2425 Titre : A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification Type de document : Article/Communication Auteurs : Z. Wang, Auteur ; Liqiang Zhang, Auteur ; Tian Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2409 - 2425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification orientée objet [Termes IGN] détection de piéton [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] lasergrammétrie [Termes IGN] objet mobile [Termes IGN] semis de points [Termes IGN] structure hiérarchique de données [Termes IGN] télémétrie laser terrestre [Termes IGN] zone urbaine dense Résumé : (Auteur) The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars. Numéro de notice : A2015-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2359951 En ligne : https://doi.org/10.1109/TGRS.2014.2359951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77533 [article]

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A critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2565 - 2586 Titre : A critical comparison among pansharpening algorithms Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Luciano Alparone, Auteur ; Jocelyn Chanussot, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2565 - 2586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de fusion [Termes IGN] analyse comparative [Termes IGN] analyse multibande [Termes IGN] analyse multirésolution [Termes IGN] état de l'art [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image satellite [Termes IGN] Matlab [Termes IGN] pansharpening (fusion d'images) [Termes IGN] qualité des données Résumé : (Auteur) Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community. Numéro de notice : A2015-523 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361734 En ligne : https://doi.org/10.1109/TGRS.2014.2361734 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77534 [article]

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Interferometric phase image estimation via sparse coding in the complex domain / Hao Hongxing in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2587 - 2602 Titre : Interferometric phase image estimation via sparse coding in the complex domain Type de document : Article/Communication Auteurs : Hao Hongxing, Auteur ; José M. Bioucas-Dias, Auteur ; Vladimir Katkovnik, Auteur Année de publication : 2015 Article en page(s) : pp 2587 - 2602 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] état de l'art [Termes IGN] filtrage du bruit [Termes IGN] image radar moirée [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] matrice creuse [Termes IGN] phase [Termes IGN] programmation par contraintes [Termes IGN] régression [Termes IGN] restauration d'image Résumé : (auteur) This paper addresses interferometric phase image estimation, i.e., the estimation of phase modulo-2π images from sinusoidal 2π-periodic and noisy observations. These degradation mechanisms make interferometric phase image estimation a quite challenging problem. We tackle this challenge by reformulating the true estimation problem as a sparse regression, often termed sparse coding, in the complex domain. Following the standard procedure in patch-based image restoration, the image is partitioned into small overlapping square patches, and the vector corresponding to each patch is modeled as a sparse linear combination of vectors, termed the atoms, taken from a set called dictionary. Aiming at optimal sparse representations, and thus at optimal noise removing capabilities, the dictionary is learned from the data that it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients) enforced by the ℓ1 norm. The effectiveness of the new sparse-coding-based approach to interferometric phase estimation, termed the SpInPHASE, is illustrated in a series of experiments with simulated and real data where it outperforms the state-of-the-art. Numéro de notice : A2015-630 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361919 En ligne : https://doi.org/10.1109/TGRS.2014.2361919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78118 [article]

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The soil moisture active passive validation experiment 2012 (SMAPVEX12): Prelaunch calibration and validation of the SMAP Soil moisture algorithms / Heather McNairn in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2784 - 2801 Titre : The soil moisture active passive validation experiment 2012 (SMAPVEX12): Prelaunch calibration and validation of the SMAP Soil moisture algorithms Type de document : Article/Communication Auteurs : Heather McNairn, Auteur ; Thomas J. Jackson, Auteur ; Grant Wiseman, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2784 - 2801 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] analyse de données [Termes IGN] bande L [Termes IGN] étalonnage [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] humidité du sol [Termes IGN] image aérienne [Termes IGN] image radar [Termes IGN] Soil Moisture Active Passive [Termes IGN] télédétection en hyperfréquence [Termes IGN] test de performance Résumé : (auteur) The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite is scheduled for launch in January 2015. In order to develop robust soil moisture retrieval algorithms that fully exploit the unique capabilities of SMAP, algorithm developers had identified a need for long-duration combined active and passive L-band microwave observations. In response to this need, a joint Canada-U.S. field experiment (SMAPVEX12) was conducted in Manitoba (Canada) over a six-week period in 2012. Several times per week, NASA flew two aircraft carrying instruments that could simulate the observations the SMAP satellite would provide. Ground crews collected soil moisture data, crop measurements, and biomass samples in support of this campaign. The objective of SMAPVEX12 was to support the development, enhancement, and testing of SMAP soil moisture retrieval algorithms. This paper details the airborne and field data collection as well as data calibration and analysis. Early results from the SMAP active radar retrieval methods are presented and demonstrate that relative and absolute soil moisture can be delivered by this approach. Passive active L-band sensor (PALS) antenna temperatures and reflectivity, as well as backscatter, closely follow dry down and wetting events observed during SMAPVEX12. The SMAPVEX12 experiment was highly successful in achieving its objectives and provides a unique and valuable data set that will advance algorithm development. Numéro de notice : A2015-631 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2364913 En ligne : https://doi.org/10.1109/TGRS.2014.2364913 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78119 [article]

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Tsunami-wave parameter estimation using GNSS-based sea surface height measurement / Kegen Yu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2603 - 2611 Titre : Tsunami-wave parameter estimation using GNSS-based sea surface height measurement Type de document : Article/Communication Auteurs : Kegen Yu, Auteur Année de publication : 2015 Article en page(s) : pp 2603 - 2611 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] estimation des paramètres [Termes IGN] hauteurs de mer [Termes IGN] longueur d'onde [Termes IGN] réflectométrie par GNSS [Termes IGN] simulation [Termes IGN] traitement de données GNSS [Termes IGN] tsunami [Termes IGN] vague Résumé : (auteur) This paper focuses on the estimation of tsunami-wave parameters (propagation direction, propagation speed, and wavelength) using the Global Navigation Satellite System (GNSS) reflectometry (GNSS-R)-based sea surface height (SSH) measurements. By exploiting multiple surface specular reflection tracks of GNSS signals as well as the geometry of wave propagation direction and the multiple tracks, concise mathematical expressions are derived to determine the propagation direction and speed and wavelength of a tsunami wave. Real tsunami-wave data measured by buoy sensors are employed to model GNSS-R-based SSH measurements by adding Gaussian measurement noise. The simulation results demonstrate that the proposed method can achieve a propagation direction estimation accuracy of about 4.4° and 5.9° when the SSH error standard deviations are 10 and 20 cm, respectively. The propagation speed estimation accuracies are about 12.7 and 17.7 m/s, respectively, under the same conditions when the speed ground truth is 200 m/s. The results also show that the wavelength estimation error can be as large as 100 km when the wavelength ground truth is about 400 km. Better filtering methods are needed to improve the wavelength estimation accuracy by mitigating the effect of the SSH estimation error particularly on the wave trailing edge of small negative magnitudes. Numéro de notice : A2015-632 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2362113 En ligne : https://doi.org/10.1109/TGRS.2014.2362113 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78120 [article]

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