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Recurrent neural network for rain estimation using commercial microwave links / Hai Victor Habi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3672 - 3681 Titre : Recurrent neural network for rain estimation using commercial microwave links Type de document : Article/Communication Auteurs : Hai Victor Habi, Auteur ; Hagit Messer, Auteur Année de publication : 2021 Article en page(s) : pp 3672 - 3681 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] dégradation du signal [Termes IGN] eau pluviale [Termes IGN] méthode robuste [Termes IGN] précision de l'estimation [Termes IGN] réseau neuronal récurrent Résumé : (Auteur) The use of recurrent neural networks (RNNs) to utilize measurements from commercial microwave links (CMLs) has recently gained attention. Whereas previous studies focused on the performance of methods for wet–dry classification, here we propose an RNN algorithm for estimating the rain-rate. We empirically analyzed the proposed algorithm, using real data, and compared it with the traditional power-law (PL)-based algorithm, commonly used for estimating rain from CML attenuation measurements. Our analysis shows that the data-driven RNN algorithm, when properly trained, outperforms the PL algorithm in terms of accuracy. On the other hand, the PL algorithm is simpler and more robust when dealing with a large variety of corruptions and adverse conditions. We then introduced a time normalization (TN) layer for controlling the trade-off between performance and robustness of the RNN methods, and demonstrated its performance. Numéro de notice : A2021-337 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3010305 Date de publication en ligne : 30/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3010305 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97568 [article]

Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3682 - 3694 Titre : Refining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data Type de document : Article/Communication Auteurs : Jia He, Auteur ; Zhizhao Liu, Auteur Année de publication : 2021 Article en page(s) : pp 3682 - 3694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Amérique du nord [Termes IGN] données météorologiques [Termes IGN] données spatiotemporelles [Termes IGN] image Terra-MODIS [Termes IGN] modèle de régression [Termes IGN] modèle de transfert radiatif [Termes IGN] précision des données [Termes IGN] station GPS [Termes IGN] vapeur d'eau Résumé : (Auteur) A new algorithm of retrieving atmospheric water vapor from MODIS near-infrared (IR) (NIR) data by using a regression fitting method based on Global Positioning System (GPS)-derived water vapor is developed in this work. The algorithm has been used to retrieve total column water vapor from Moderate Resolution Imaging Spectroradiometer (MODIS) satellites both Terra and Aqua under cloud-free conditions from solar radiation in the NIR channels. Water vapor data estimated from GPS observations recorded from 2003 to 2017 by the SuomiNet GPS network over the western North America are used as ground truth references. The GPS stations were classified into six subsets based on the surface types adopted from MCD12Q1 IGBP legend. The differences in surface types are considered in the regression fitting procedure, thus different regression functions are trained for different surface types. Thus, the wet bias in the operational MODIS water vapor products has been significantly reduced. Water vapor retrieved from each of the three absorption channels and the weighted water vapor of combined three absorption channels are analyzed. Validation shows that the weighted water vapor performs better than the single-channel results. Compared to the MODIS/Terra water vapor products, the RMSE has been reduced by 50.78% to 2.229 mm using the two-channel ratio transmittance method and has been reduced by 53.06% to 2.126 mm using the three-channel ratio transmittance method. Compared to the MODIS/Aqua water vapor products, the RMSE has been reduced by 45.54% to 2.423 mm using the two-channel ratio transmittance method and has been reduced by 45.34% to 2.432 mm using the three-channel ratio transmittance method. Numéro de notice : A2021-338 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3016655 Date de publication en ligne : 24/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3016655 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97569 [article]

Evaluating P-Band TomoSAR for biomass retrieval in boreal forest / Erik Blomberg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3793 - 3804 Titre : Evaluating P-Band TomoSAR for biomass retrieval in boreal forest Type de document : Article/Communication Auteurs : Erik Blomberg, Auteur ; Lars M.H. Ulander, Auteur ; Stefano Tebaldini, Auteur ; Laurent Ferro-Famil, Auteur Année de publication : 2021 Article en page(s) : pp 3793 - 3804 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande P [Termes IGN] biomasse forestière [Termes IGN] forêt boréale [Termes IGN] Suède [Termes IGN] tomographie radar Résumé : (Auteur) P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity. Numéro de notice : A2021-339 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020775 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020775 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97570 [article]

An improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm / Jian Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3725 - 3736 Titre : An improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm Type de document : Article/Communication Auteurs : Jian Kong, Auteur ; Lulu Shan, Auteur ; Chen Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3725 - 3736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] erreur absolue [Termes IGN] filtre de Kalman [Termes IGN] fusion de données multisource [Termes IGN] modèle ionosphérique [Termes IGN] modèle stochastique [Termes IGN] perturbation ionosphérique [Termes IGN] tempête magnétique [Termes IGN] teneur totale en électrons [Termes IGN] tomographie Résumé : (auteur) Global Navigation Satellite System (GNSS) ionospheric tomography is a typical ill-posed problem. Joint inversion with external observation data is one of the effective ways to mitigate the problem. In this article, by fusing 3-D multisource ionospheric data, and improving the stochastic model, an improved GNSS tomographic algorithm MFCIT [computerized ionospheric tomography (CIT) using mapping function] is presented. The accuracy of the algorithm is validated by selected data under different geomagnetic and solar conditions acquired in Europe. The results show that the estimated, statistically significant uncertainty for each of the layers is about 0.50–3.0TECU, with the largest absolute error within 6.0TECU. The advantage of the MFCIT is that it is based on the Kalman filter, which enables efficient near real-time 3-D monitoring of ionosphere. The temporal resolution can reach ~1 min level. Here, we apply the ionospheric tomography inversion to the magnetic storm on January 7, 2015, in the European region, and quantified the evolution of the storm. The results show that the difference of the core region between the MFCIT and CODE GIM is less than 1TECU. More importantly, during the initial phase of the storm, when the ionospheric disturbance is not evident in the single layer CODE GIM model, the MFCIT shows obvious positive disturbances in the upper ionosphere, although there is no disturbance in the F2 layer. The MFCIT further tracks the evolution of the magnetic storm that the ionospheric disturbance expands from the upper to the lower ionosphere layers, and at UT12:00, the disturbance continues to spread to the F2 layer. Numéro de notice : A2021-396 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022949 Date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3022949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97686 [article]

SAR speckle removal using hybrid frequency modulations / Shuaiqi Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3956 - 3966 Titre : SAR speckle removal using hybrid frequency modulations Type de document : Article/Communication Auteurs : Shuaiqi Liu, Auteur ; Lele Gao, Auteur ; Yu Lei, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3956 - 3966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] artefact [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtrage du bruit [Termes IGN] filtre de déchatoiement [Termes IGN] image radar moirée [Termes IGN] modulation de fréquence Résumé : (auteur) Synthetic aperture radar (SAR) images often interfere with speckle artifacts that have a great impact on subsequent processing and analysis operations. To remove speckle artifacts, this article introduces a hybrid denoising approach by using a convolutional neural network (CNN) and consistent cycle spinning (CCS) in the nonsubsample shearlet transform (NSST) domain. First, we apply NSST to a noisy SAR image to gain low- and high-frequency coefficients. Second, we adopt a learned deep CNN model to eliminate the speckle noise in the low-frequency coefficients, which retains more contour information. Third, we employ CCS to enhance the high-frequency coefficients, which preserves more details of the original SAR image. Finally, we obtain the denoised image by using inverse NSST applied to the denoised coefficients. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only achieves better speckle removal performance but also maintains more detailed information retention. Numéro de notice : A2021-397 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3014130 Date de publication en ligne : 18/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3014130 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97688 [article]