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SNR-based water height retrieval in rivers: Application to high amplitude asymmetric tides in the Garonne river / Pierre Zeiger in Remote sensing, vol 13 n° 9 (May 2021)  

Public [article]  inRemote sensing > vol 13 n° 9 (May 2021) . - n° 1856 Titre : SNR-based water height retrieval in rivers: Application to high amplitude asymmetric tides in the Garonne river Type de document : Article/Communication Auteurs : Pierre Zeiger, Auteur ; Frédéric Frappart, Auteur ; José Darrozes, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1856 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] crue [Termes IGN] filtrage du bruit [Termes IGN] Garonne (bassin) [Termes IGN] interférence [Termes IGN] marée océanique [Termes IGN] modèle d'inversion [Termes IGN] rapport signal sur bruit [Termes IGN] réflectométrie par GNSS [Termes IGN] série temporelle [Termes IGN] surveillance hydrologique Résumé : (auteur) Signal-to-noise ratio (SNR) time series acquired by a geodetic antenna were analyzed to retrieve water heights during asymmetric tides on a narrow river using the Interference Pattern Technique (IPT) from Global Navigation Satellite System Reflectometry (GNSS-R). The dynamic SNR method was selected because the elevation rate of the reflecting surface during rising tides is high in the Garonne River with macro tidal conditions. A new process was developed to filter out the noise introduced by the environmental conditions on the reflected signal due to the narrowness of the river compared to the size of the Fresnel areas, the presence of vegetation on the river banks, and the presence of boats causing multiple reflections. This process involved the removal of multipeaks in the Lomb-Scargle Periodogram (LSP) output and an iterative least square estimation (LSE) of the output heights. Evaluation of the results was performed against pressure-derived water heights. The best results were obtained using all GNSS bands (L1, L2, and L5) simultaneously: R = 0.99, ubRMSD = 0.31 m. We showed that the quality of the retrieved heights was consistent, whatever the vertical velocity of the reflecting surface, and was highly dependent on the number of satellites visible. The sampling period of our solution was 1 min with a 5-min moving window, and no tide models or fit were used in the inversion process. This highlights the potential of the dynamic SNR method to detect and monitor extreme events with GNSS-R, including those affecting inland waters such as flash floods. Numéro de notice : A2021-406 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs13091856 date de publication en ligne : 10/05/2021 En ligne : https://doi.org/10.3390/rs13091856 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97722 [article]

A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n° 3 (May 2021) . - n° 75 Titre : A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms Type de document : Article/Communication Auteurs : Dimitrios Bellos, Auteur ; Mark Basham, Auteur ; Tony Pridmore, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] acquisition de connaissances [Termes IGN] apprentissage profond [Termes IGN] échantillonnage [Termes IGN] filtrage du bruit [Termes IGN] rapport signal sur bruit [Termes IGN] rayon X [Termes IGN] reconstruction d'image [Termes IGN] segmentation sémantique [Termes IGN] série temporelle [Termes IGN] tomographie Résumé : (auteur) Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available. Numéro de notice : A2021-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01196-4 date de publication en ligne : 27/04/2021 En ligne : https://doi.org/10.1007/s00138-021-01196-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97902 [article]

Denoising Sentinel-1 extra-wide mode cross-polarization images over sea ice / Yan Sun in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2116 - 2131 Titre : Denoising Sentinel-1 extra-wide mode cross-polarization images over sea ice Type de document : Article/Communication Auteurs : Yan Sun, Auteur ; Xiao-Ming Li, Auteur Année de publication : 2021 Article en page(s) : pp 2116 - 2131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Austral (océan) [Termes IGN] bruit thermique [Termes IGN] étalonnage radiométrique [Termes IGN] filtrage du bruit [Termes IGN] glace de mer [Termes IGN] image Sentinel-SAR [Termes IGN] image TOPSAR [Termes IGN] polarisation croisée [Termes IGN] rapport signal sur bruit Résumé : (Auteur) Sentinel-1 (S1) extra-wide (EW) swath data in cross-polarization (horizontal–vertical, HV or vertical–horizontal, VH) are strongly affected by the scalloping effect and thermal noise, particularly over areas with weak backscattered signals, such as sea surfaces. Although noise vectors in both the azimuth and range directions are provided in the standard S1 EW data for subtraction, the residual thermal noise still significantly affects sea ice detection by the EW data. In this article, we improve the denoising method developed in previous studies to remove the additive noise for the S1 EW data in cross-polarization. Furthermore, we propose a new method for eliminating the residual noise (i.e., multiplicative noise) at the subswath boundaries of the EW data, which cannot be well processed by simply subtracting the reconstructed 2-D noise field. The proposed method of removing both the additive and multiplicative noise was applied to EW HV-polarized images processed using different Instrument Processing Facility (IPF) versions. The results suggest that the proposed algorithm significantly improves the quality of EW HV-polarized images under various sea ice conditions and sea states in the marginal ice zone (MIZ) of the Arctic. This is of great support for the utilization of cross-polarization synthetic aperture radar (SAR) images in wide swaths for intensive sea ice monitoring in polar regions. Numéro de notice : A2021-214 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3005831 date de publication en ligne : 09/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3005831 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97202 [article]

Radar measurements of snow depth over sea ice on an unmanned aerial vehicle / Adrian Eng-Choon Tan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 1868 - 1875 Titre : Radar measurements of snow depth over sea ice on an unmanned aerial vehicle Type de document : Article/Communication Auteurs : Adrian Eng-Choon Tan, Auteur ; Josh McCulloch, Auteur ; Wolfgang Rack, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1868 - 1875 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Austral (océan) [Termes IGN] épaisseur [Termes IGN] glace de mer [Termes IGN] image captée par drone [Termes IGN] image radar [Termes IGN] manteau neigeux [Termes IGN] précision centimétrique [Termes IGN] rapport signal sur bruit [Termes IGN] variation saisonnière Résumé : (Auteur) We propose a lightweight radar that autonomously measures snow depth over sea ice from an unmanned aerial vehicle (UAV). Development of this snow radar and its integration with an octocopter UAV is presented. Field trials of the UAV-mounted snow radar, conducted in Antarctica during the summer season of 2017/2018, are also described. The radar allows measurements of snow depths on sea ice between 10 and 100 cm. Additional reflections due to internal layers within the snow are evident at a few measurement points. The snow radar is evaluated for various flight parameters: stationary; flying at speeds between 1 and 3 m/s, and at heights from 5 to 15 m. Evaluation of snow-depth results indicates that a depth accuracy of ±3.2 cm is achieved with stationary measurements, and of ±9.1 cm with measurements at the various flight speeds. Numéro de notice : A2021-212 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3006182 date de publication en ligne : 14/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3006182 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97197 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1106 - 1117 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 hyperfréquence Résumé : (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 [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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Evaluation of multipath mitigation performance using signal-to-noise ratio (SNR) based signal selection methods / Valanon Uaratanawong in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

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Underwater object detection and reconstruction based on active single-pixel imaging and super-resolution convolutional neural network / Mengdi Li in Sensors, vol 21 n° 1 (January 2021)  

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Acquisition of weak GPS signals using wavelet-based de-noising methods / Mohaddeseh Sharie in Survey review, vol 52 n° 375 (November 2020)  

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A low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments / Elahe S. Abdolkarimi in GPS solutions, Vol 24 n° 4 (October 2020)  

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Python software tools for GNSS interferometric reflectometry (GNSS-IR) / Angel Martín in GPS solutions, Vol 24 n° 4 (October 2020)  

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Semi-automatic building extraction from WorldView-2 imagery using taguchi optimization / Hasan Tonbul in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 9 (September 2020)  

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Ship detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

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Wavelet-adaptive neural subtractive clustering fuzzy inference system to enhance low-cost and high-speed INS/GPS navigation system / Elahe S. Abdolkarimi in GPS solutions, vol 24 n° 2 (April 2020)  

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Dimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])  

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