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Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping / Jiangsan Zhao in Remote sensing, vol 14 n° 5 (March-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 5 (March-1 2022) . - n° 1272; Titre : Deep-learning-based multispectral image reconstruction from single natural color RGB image - Enhancing UAV-based phenotyping Type de document : Article/Communication Auteurs : Jiangsan Zhao, Auteur ; Ajay Kumar, Auteur ; Balaji Naik Banoth, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1272; Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agriculture de précision [Termes IGN] apprentissage profond [Termes IGN] erreur absolue [Termes IGN] image multibande [Termes IGN] image RVB [Termes IGN] Inde [Termes IGN] phénologie [Termes IGN] reconstruction d'image Résumé : (auteur) Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. Numéro de notice : A2022-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14051272 Date de publication en ligne : 05/03/2022 En ligne : https://doi.org/10.3390/rs14051272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100033 [article]

Conventional and neural network-based water vapor density model for GNSS troposphere tomography / Chen Liu in GPS solutions, vol 26 n° 1 (January 2022)  

Public [article]  inGPS solutions > vol 26 n° 1 (January 2022) . - n° 4 Titre : Conventional and neural network-based water vapor density model for GNSS troposphere tomography Type de document : Article/Communication Auteurs : Chen Liu, Auteur ; Yibin Yao, Auteur ; Chaoqian Xu, Auteur Année de publication : 2022 Article en page(s) : n° 4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] classification par réseau neuronal [Termes IGN] erreur absolue [Termes IGN] étalonnage de modèle [Termes IGN] modèle météorologique [Termes IGN] propagation troposphérique [Termes IGN] tomographie par GPS [Termes IGN] vapeur d'eau [Termes IGN] voxel Résumé : (auteur) Global navigation satellite system (GNSS) water vapor (WV) tomography is a promising technique to reconstruct the three-dimensional (3D) WV field. However, this technique usually suffers from the ill-posed problem caused by the poor geometry of GNSS rays, resulting in underdetermined tomographic equations. Such equations often rely on iterative methods for solving, but conventional iterative approaches require accurate initial WV density. To address this demand, we proposed two models for WV density estimation. One is the conventional model (CO model) that consists of an exponential model and a linear least-squares model, which are used to describe the spatial and temporal variability of the WV density, respectively. The other is a neural network model (NN model) that uses a backpropagation neural network (BPNN) to fit the nonlinear variation of WV density in both spatial and temporal domains. WV density derived from a Hong Kong (HK) radiosonde station (RS) during 2020 was used to validate the proposed models. Validation results show that both models well describe the spatial and temporal distribution of the WV density. The NN model exhibits better prediction performance than the CO model in terms of root mean square error (RMSE) and bias. We also applied the proposed models to GNSS WV tomography to test their performance in extreme weather conditions. Test results show that the proposed model-based GNSS tomography can correct the content of WV density but cannot accurately sense its irregular distribution. Numéro de notice : A2022-005 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01188-x Date de publication en ligne : 23/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01188-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98920 [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]

A lightweight ensemble spatiotemporal interpolation model for geospatial data / Shifen Cheng in International journal of geographical information science IJGIS, vol 34 n° 9 (September 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 9 (September 2020) . - pp 1849 - 1872 Titre : A lightweight ensemble spatiotemporal interpolation model for geospatial data Type de document : Article/Communication Auteurs : Shifen Cheng, Auteur ; Peng Peng, Auteur ; Feng Lu, Auteur Année de publication : 2020 Article en page(s) : pp 1849 - 1872 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] coefficient de corrélation [Termes IGN] distance pondérée [Termes IGN] données localisées [Termes IGN] erreur absolue [Termes IGN] interpolation spatiale [Termes IGN] lissage de données [Termes IGN] modélisation spatio-temporelle [Termes IGN] requête spatiotemporelle Résumé : (auteur) Missing data is a common problem in the analysis of geospatial information. Existing methods introduce spatiotemporal dependencies to reduce imputing errors yet ignore ease of use in practice. Classical interpolation models are easy to build and apply; however, their imputation accuracy is limited due to their inability to capture spatiotemporal characteristics of geospatial data. Consequently, a lightweight ensemble model was constructed by modelling the spatiotemporal dependencies in a classical interpolation model. Temporally, the average correlation coefficients were introduced into a simple exponential smoothing model to automatically select the time window which ensured that the sample data had the strongest correlation to missing data. Spatially, the Gaussian equivalent and correlation distances were introduced in an inverse distance-weighting model, to assign weights to each spatial neighbor and sufficiently reflect changes in the spatiotemporal pattern. Finally, estimations of the missing values from temporal and spatial were aggregated into the final results with an extreme learning machine. Compared to existing models, the proposed model achieves higher imputation accuracy by lowering the mean absolute error by 10.93 to 52.48% in the road network dataset and by 23.35 to 72.18% in the air quality station dataset and exhibits robust performance in spatiotemporal mutations. Numéro de notice : A2020-484 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725016 Date de publication en ligne : 12/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95651 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2020091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips / Wanke Liu in Advances in space research, vol 63 n° 9 (1 May 2019)  

Public [article]  inAdvances in space research > vol 63 n° 9 (1 May 2019) . - pp 2724 - 2734 Titre : An improved robust Kalman filtering strategy for GNSS kinematic positioning considering small cycle slips Type de document : Article/Communication Auteurs : Wanke Liu, Auteur ; Jianlong Li, Auteur ; Qi Zeng, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 2724 - 2734 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] erreur absolue [Termes IGN] erreur de positionnement [Termes IGN] filtre de Kalman [Termes IGN] glissement de cycle [Termes IGN] matrice de covariance [Termes IGN] phase [Termes IGN] positionnement cinématique [Termes IGN] positionnement différentiel [Termes IGN] positionnement par GNSS [Termes IGN] résidu [Termes IGN] valeur aberrante Résumé : (auteur) In GNSS (Global Navigation Satellite Systems) kinematic positioning, observations will be inevitably contaminated by cycle slips and gross errors, as the complex observation environment changes rapidly. These outliers will degrade the performance of classic Kalman filtering applied in GNSS kinematic resolution and eventually, the filtering may converge slowly or even diverge and thus the precision will be degraded. Therefore, a robust Kalman filter should be applied to resist the influence of these outliers that cannot be identified in the data preprocessing stage. Based on the conventional IGG (Institute of Geodesy and Geophysics) III equivalent weight method which addresses the outliers of the zero-weight segment with the same strategy, this paper proposes an improved robust Kalman filtering strategy that detects outliers by both posterior phase residuals and standardized residuals and handles the carrier-phase observation of zero-weight segment as cycle slips. In addition, to avoid unnecessary ambiguity reinitialization caused by the detected cycle slips, only when the carrier-phase observation of the same satellite is classified in the zero-weight segment over two consecutive epochs should the ambiguity be reinitialized. Experimental results of relative positioning show that the improved method can not only mitigate the influence of unexpected outliers in the Kalman filter but also improve the fixing rate of ambiguity resolution as well as the accuracy and reliability of positioning. Numéro de notice : A2019-396 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2017.11.041 Date de publication en ligne : 08/12/2017 En ligne : https://doi.org/10.1016/j.asr.2017.11.041 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93505 [article]

Per-pixel bias-variance decomposition of continuous errors in data-driven geospatial modeling : A case study in environmental remote sensing / Jing Gao in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

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Estimating the count of completeness errors in geographic data sets by means of a generalized Waring regression model / Francisco Javier Ariza-López in International journal of geographical information science IJGIS, vol 29 n° 8 (August 2015)  

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A comparison of four common atmospheric correction methods / A.S. Mahiny in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 4 (April 2007)  

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Systematic effects in absolute chamber calibration of GPS antennas / Philipp Zeimetz in Geomatica, vol 60 n° 3 (September 2006)  

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Analyse et analyse numérique / Luc Jolivet (2005)

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Ordering points for incremental TIN construction from DEMs / J.J. Little in Geoinformatica, vol 7 n° 1 (March - May 2003)  

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Status of the SRTM data processing: when will the world-wild 30m DTM data be available? / M. Werner in GIS Geo-Informations-Systeme, vol 2001 n° 12 (Dezember 2001)

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The calibration of SAR interferometric DEM's with Differential GPS / S. Slob in GeoEurope, vol 9 n° 6 (01/06/2000)

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Télémétrie spatiale par station laser / Jean Gaignebet in L'Ingénieur constructeur ETP, n° 191 (Juillet 1974)

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