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[n° ou bulletin]
est un bulletin de IEEE Transactions on geoscience and remote sensing / IEEE Geoscience and remote sensing society (Etats-Unis) (1986 -) ![]()
[n° ou bulletin]
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Dépouillements


Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active–Passive Satellite and evaluation at core validation sites / Seung-Bum Kim in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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[article]
Titre : Surface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active–Passive Satellite and evaluation at core validation sites Type de document : Article/Communication Auteurs : Seung-Bum Kim, Auteur ; Joel T. Johnson, Auteur ; Mahta Moghaddam, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1897 - 1914 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] croissance végétale
[Termes IGN] données hétérogènes
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] mission SMAP
[Termes IGN] pente
[Termes IGN] polarisation
[Termes IGN] problème inverse
[Termes IGN] série temporelleRésumé : (Auteur) This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and -0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, -0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation. Numéro de notice : A2017-169 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2631126 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2631126 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84713
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 1897 - 1914[article]Analysis of Galileo and GPS integration for GNSS tomography / Pedro Benevides in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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[article]
Titre : Analysis of Galileo and GPS integration for GNSS tomography Type de document : Article/Communication Auteurs : Pedro Benevides, Auteur ; G. Nico, Auteur ; J. Catalão, Auteur Année de publication : 2017 Article en page(s) : pp 1936 - 1943 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] angle azimutal
[Termes IGN] atmosphère terrestre
[Termes IGN] données Galileo
[Termes IGN] humidité de l'air
[Termes IGN] intégration de données
[Termes IGN] Lisbonne
[Termes IGN] reconstruction 3D
[Termes IGN] réfraction atmosphérique
[Termes IGN] tomographie par GPSRésumé : (Auteur) Global Navigation Satellite System (GNSS) tomography provides 3-D reconstructions of atmosphere wet refractivity, related to water vapor. A simulated analysis of the integration of Global Positioning System and future Galileo data is presented. Atmospheric refractivity is derived from radiosonde data acquired over the Lisbon area. The impact of Galileo data on the tomographic reconstruction is assessed. Furthermore, horizontal anomalies are added to a reference vertical profile of atmospheric refractivity to reproduce low-level dry or wet air intrusions, a phenomenon commonly observed in meteorological data acquired by both radiosonde and satellites. The dependence of tomographic solution on the GNSS network density is also analyzed. Better reconstruction capabilities in the lower layers are observed when increasing the network density. Numéro de notice : A2017-170 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2631449 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2631449 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84714
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 1936 - 1943[article]Estimation of 3-D surface displacement based on InSAR and deformation modeling / Jun Hu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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[article]
Titre : Estimation of 3-D surface displacement based on InSAR and deformation modeling Type de document : Article/Communication Auteurs : Jun Hu, Auteur ; Xiao-Li Ding, Auteur ; Lei Zhang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 2007 - 2016 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] déformation de surface
[Termes IGN] dynamique des fluides
[Termes IGN] élasticité
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] modélisation 3D
[Termes IGN] test de performanceRésumé : (Auteur) A new approach is presented for mapping 3-D surface displacement caused by subsurface fluid volumetric change based on 1-D interferometric synthetic aperture radar (InSAR) line-of-sight measurements and surface deformation modeling. The relationship between surface deformation and source fluid volumetric change is modeled according to elastic half-space theory. A distinctive advantage of the proposed approach is that it effectively extends the capability of the sun-synchronous orbit side-looking synthetic aperture radar that has been essentially only able to measure 1-D displacements accurately or at most 2-D displacements when InSAR measurements from more than one orbit or platform are combined. Experimental studies are carried out with both simulated and real data sets to test the performance of the method. The results have demonstrated that the approach works very well. Numéro de notice : A2017-171 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2634087 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2634087 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84715
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2007 - 2016[article]Hyperspectral band selection from statistical wavelet models / Siwei Feng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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[article]
Titre : Hyperspectral band selection from statistical wavelet models Type de document : Article/Communication Auteurs : Siwei Feng, Auteur ; Yuki Itoh, Auteur ; Mario Parente, Auteur ; Marco F. Duarte, Auteur Année de publication : 2017 Article en page(s) : pp 2111 - 2123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chaîne de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification spectrale
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] redondance de données
[Termes IGN] signature spectraleRésumé : (Auteur) High spectral resolution brings hyperspectral images with large amounts of information, which makes these images more useful in many applications than images obtained from traditional multispectral scanners with low spectral resolution. However, the high data dimensionality of hyperspectral images increases the burden on data computation, storage, and transmission; fortunately, the high redundancy in the spectral domain allows for significant dimensionality reduction. Band selection provides a simple dimensionality reduction scheme by discarding bands that are highly redundant, thereby preserving the structure of the data set. This paper proposes a new criterion for pointwise-ranking-based band selection that uses a nonhomogeneous hidden Markov chain (NHMC) model for redundant wavelet coefficients of each hyperspectral signature. The model provides a binary multiscale label that encodes semantic features that are useful to discriminate spectral types. A band ranking score considers the average correlation among the average NHMC labels for each band. We also test richer discrete-valued label vectors that provide a more finely grained quantization of spectral fluctuations. In addition, since band selection methods based on band ranking often ignore correlations in selected bands, we study the effect of redundancy elimination, applied on the selected features, on the performance of an example classification problem. Our experimental results also include an optional redundancy elimination step and test their effect on classification performance that is based on the selected bands. The experimental results also include a comparison with several relevant supervised band selection techniques. Numéro de notice : A2017-172 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2636850 En ligne : https://doi.org/10.1109/TGRS.2016.2636850 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84717
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2111 - 2123[article]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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[article]
Titre : Statistical atmospheric parameter retrieval largely benefits from spatial–spectral image compression Type de document : Article/Communication Auteurs : Joaquín García-Sobrino, Auteur ; Joan Serra-Sagristà, Auteur ; Valero Laparra, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp. 2213 - 2224 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] compression d'image
[Termes IGN] données météorologiques
[Termes IGN] humidité de l'air
[Termes IGN] image infrarouge couleur
[Termes IGN] image MetOp-IASI
[Termes IGN] interférométrie
[Termes IGN] température de l'airRésumé : (Auteur) The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors. Numéro de notice : A2017-173 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2639099 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2639099 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84722
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp. 2213 - 2224[article]Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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[article]
Titre : Unsupervised feature learning for land-use scene recognition Type de document : Article/Communication Auteurs : Jiayuan Fan, Auteur ; Tao Chen, Auteur ; Shijian Lu, Auteur Année de publication : 2017 Article en page(s) : pp 2250 - 2261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse discriminante
[Termes IGN] codage
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] invariant
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] reconnaissance automatique
[Termes IGN] Singapour
[Termes IGN] utilisation du solRésumé : (Auteur) This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced landuse RGB data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use RGB-NIR data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods. Numéro de notice : A2107-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2640186 En ligne : https://doi.org/10.1109/TGRS.2016.2640186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84723
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2250 - 2261[article]Trace coherence : a new operator for polarimetric and interferometric SAR images / Armando Marino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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Titre : Trace coherence : a new operator for polarimetric and interferometric SAR images Type de document : Article/Communication Auteurs : Armando Marino, Auteur Année de publication : 2017 Article en page(s) : pp 2326 - 2339 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification
[Termes IGN] cohérence des données
[Termes IGN] détection de changement
[Termes IGN] image radar moirée
[Termes IGN] Pol-INSAR
[Termes IGN] polarimétrie radarRésumé : (Auteur) Quadratic forms play an important role in the development of several polarimetric and interferometric synthetic aperture radar (Pol-InSAR) methodologies, which are very powerful tools for earth observation. This paper investigates integrals of Pol-InSAR operators based on quadratic forms, with special interest on the Pol-InSAR coherence. A new operator, namely Trace Coherence, is introduced, which provides an approximation for the center of mass of the coherence region (CoRe). The latter is the locus of points on the polar plot containing all the possible coherence values. Such center of mass can be calculated as the integral of Pol-InSAR coherences over the scattering mechanisms (SMs). The trace coherence provides synthetic information regarding the partial target as one single entity. Therefore, it provides a representation, which is not dependent on the selection of one specific polarization channel. It may find application in change detection (e.g., coherent change detection and differential DEM), classification (e.g., building structure parameters), and modeling (e.g., for the retrieval of forest height). In calculating the integral of the Pol-InSAR coherences, an approximate trace coherence expression is derived and shown to improve the calculation speed by several orders of magnitude. The trace coherence approximation is investigated using Monte Carlo simulations and validated ESA (DLR) L-band quad-polarimetric data acquired during the AGRISAR 2006 campaign. The result of the analysis using simulated and real data is that the average error in approximating the integral of the coherence region is 0.025 in magnitude and 3° in phase (in scenarios with sufficiently high coherence). Numéro de notice : A2017-175 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2641742 En ligne : https://doi.org/10.1109/TGRS.2016.2641742 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84747
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2326 - 2339[article]Deep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
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[article]
Titre : Deep supervised and contractive neural network for SAR image classification Type de document : Article/Communication Auteurs : Jie Geng, Auteur ; Hongyu Wang, Auteur ; Jianchao Fan, Auteur ; Xiaorui Ma, Auteur Année de publication : 2017 Article en page(s) : pp 2442 - 2459 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] algorithme Graph-Cut
[Termes IGN] analyse discriminante
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtre de déchatoiement
[Termes IGN] filtre de Gabor
[Termes IGN] image radar moirée
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)Résumé : (Auteur) The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches. Numéro de notice : A2017-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2645226 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2645226 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84748
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2442 - 2459[article]