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G-band radar for humidity and cloud remote sensing / Ken B. Cooper in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
[article]
Titre : G-band radar for humidity and cloud remote sensing Type de document : Article/Communication Auteurs : Ken B. Cooper, Auteur ; Richard J. Roy, Auteur ; Robert Dengler, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1106 - 1117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] antenne radar
[Termes IGN] bruit thermique
[Termes IGN] humidité de l'air
[Termes IGN] modèle atmosphérique
[Termes IGN] nuage
[Termes IGN] rapport signal sur bruit
[Termes IGN] réflectivité
[Termes IGN] télédétection en hyperfréquenceRésumé : (auteur) VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated continuous-wave (FMCW) radar mode, offering a transmit power of 200–300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167–174.8 GHz results in a nominal range resolution of 15 m. The radar’s measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as −26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR’s absolute sensitivity was validated using a spherical metal target in the radar antenna’s far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver. Numéro de notice : A2021-112 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2995325 Date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2995325 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96916
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1106 - 1117[article]Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
[article]
Titre : Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification Type de document : Article/Communication Auteurs : Zitong Wu, Auteur ; Biao Hou, Auteur ; Licheng Jiao, Auteur Année de publication : 2021 Article en page(s) : pp 1200 - 1213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification contextuelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image radar moiréeRésumé : (auteur) Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. Numéro de notice : A2021-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3004911 Date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3004911 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96918
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1200 - 1213[article]Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China / Lu Miao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)
[article]
Titre : Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China Type de document : Article/Communication Auteurs : Lu Miao, Auteur ; Kailiang Deng, Auteur ; Guangcai Feng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 105-116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] aéroport
[Termes IGN] coin réflecteur
[Termes IGN] déformation d'édifice
[Termes IGN] déformation de surface
[Termes IGN] données spatiotemporelles
[Termes IGN] image Envisat-ASAR
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] piste d'aéroport
[Termes IGN] Shenzhen
[Termes IGN] surveillance d'ouvrage
[Termes IGN] surveillance géologiqueRésumé : (Auteur) Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamic-load settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport. Numéro de notice : A2021-098 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.2.105 Date de publication en ligne : 01/02/2021 En ligne : https://doi.org/10.14358/PERS.87.2.105 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97042
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 2 (February 2021) . - pp 105-116[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 105-2021021 SL Revue Centre de documentation Revues en salle Disponible SAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
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Titre : SAR image speckle reduction based on nonconvex hybrid total variation model Type de document : Article/Communication Auteurs : Yuli Sun, Auteur ; Lin Lei, Auteur ; Dongdong Guan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1231 - 1249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] artefact
[Termes IGN] chatoiement
[Termes IGN] détection de contours
[Termes IGN] distribution de Fisher
[Termes IGN] gradient
[Termes IGN] image radar moirée
[Termes IGN] régularisation d'image
[Termes IGN] variableRésumé : (auteur) Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ℓ1 -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ℓ1 -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called Fisher–Tippett (FT) distribution- ℓp -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method. Numéro de notice : A2021-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002561 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002561 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96924
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1231 - 1249[article]Study of systematic bias in measuring surface deformation with SAR interferometry / Homa Ansari in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
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Titre : Study of systematic bias in measuring surface deformation with SAR interferometry Type de document : Article/Communication Auteurs : Homa Ansari, Auteur ; Francesco De Zan, Auteur ; Alessandro Parizzi, Auteur Année de publication : 2021 Article en page(s) : pp 1285 - 1301 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] atténuation du signal
[Termes IGN] décorrélation
[Termes IGN] déformation de surface
[Termes IGN] erreur de phase
[Termes IGN] erreur systématique
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] processus stochastique
[Termes IGN] rapport signal sur bruit
[Termes IGN] série temporelleRésumé : (auteur) This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms. Numéro de notice : A2021-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003421 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96929
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1285 - 1301[article]Evaluation of a neural network with uncertainty for detection of ice and water in SAR imagery / Nazanin Asadi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkEvaluation du stock de carbone aérien dans la végétation à partir de multiples observations satellites micro-ondes / Martin Cubaud (2021)PermalinkHolographic SAR tomography 3-D reconstruction based on iterative adaptive approach and generalized likelihood ratio test / Dong Feng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkImpact of forest disturbance on InSAR surface displacement time series / Paula M. Bürgi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkPermalinkPermalinkSAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery / Marie Ballère in Remote sensing of environment, Vol 252 (January 2021)PermalinkSemantic segmentation of sea ice type on Sentinel-1 SAR data using convolutional neural networks / Alissa Kouraeva (2021)PermalinkSuivi de la déforestation à partir de données Sentinel-1 en contexte tropical / Lucile Auzeméry (2021)PermalinkMonitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas / Nadia Ouaadi in Remote sensing of environment, Vol 251 (15 December 2020)Permalink