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DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images / Yingjie Wang in Remote sensing of environment, vol 274 (June 2022)  

Public [article]  inRemote sensing of environment > vol 274 (June 2022) . - n° 112973 Titre : DART-Lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images Type de document : Article/Communication Auteurs : Yingjie Wang, Auteur ; Abdelaziz Kallel, Auteur ; Xuebo Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112973 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes IGN] image à haute résolution [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de transfert radiatif [Termes IGN] radiance [Termes IGN] réflectance directionnelle [Termes IGN] scène forestière [Termes IGN] scène urbaine Résumé : (auteur) Accurate and efficient simulation of remote sensing images is increasingly needed in order to better exploit remote sensing observations and to better design remote sensing missions. DART (Discrete Anisotropic Radiative Transfer), developed since 1992 based on the discrete ordinates method (i.e., standard mode DART-FT), is one of the most accurate and comprehensive 3D radiative transfer models to simulate the radiative budget and remote sensing observations of urban and natural landscapes. Recently, a new method, called DART-Lux, was integrated into DART model to address the requirements of massive remote sensing data simulation for large-scale and complex landscapes. It is developed based on efficient Monte Carlo light transport algorithms (i.e., bidirectional path tracing) and on DART model framework. DART-Lux can accurately and rapidly simulate the bidirectional reflectance factor (BRF) and spectral images of arbitrary landscapes. This paper presents its theory, implementation, and evaluation. Its accuracy, efficiency and advantages are also discussed. The comparison with standard DART-FT in a variety of scenarios shows that DART-Lux is consistent with DART-FT (relative differences Numéro de notice : A2022-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112973 Date de publication en ligne : 26/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112973 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100698 [article]

Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 187 (May 2022) . - pp 294 - 305 Titre : Multi-modal temporal attention models for crop mapping from satellite time series Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Année de publication : 2022 Projets : 3-projet - voir note / Article en page(s) : pp 294 - 305 Note générale : bibliographie This work was partly supported by ASP, the French Payment Agency. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] attention (apprentissage automatique) [Termes IGN] bande C [Termes IGN] carte agricole [Termes IGN] fusion d'images [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] parcelle agricole [Termes IGN] Pastis [Termes IGN] segmentation d'image [Termes IGN] série temporelle [Termes IGN] surface cultivée Résumé : (auteur) Optical and radar satellite time series are synergetic: optical images contain rich spectral information, while C-band radar captures useful geometrical information and is immune to cloud cover. Motivated by the recent success of temporal attention-based methods across multiple crop mapping tasks, we propose to investigate how these models can be adapted to operate on several modalities. We implement and evaluate multiple fusion schemes, including a novel approach and simple adjustments to the training procedure, significantly improving performance and efficiency with little added complexity. We show that most fusion schemes have advantages and drawbacks, making them relevant for specific settings. We then evaluate the benefit of multimodality across several tasks: parcel classification, pixel-based segmentation, and panoptic parcel segmentation. We show that by leveraging both optical and radar time series, multimodal temporal attention-based models can outmatch single-modality models in terms of performance and resilience to cloud cover. To conduct these experiments, we augment the PASTIS dataset (Garnot and Landrieu, 2021a) with spatially aligned radar image time series. The resulting dataset, PASTIS-R, constitutes the first large-scale, multimodal, and open-access satellite time series dataset with semantic and instance annotations. (Dataset available at: https://zenodo.org/record/5735646) Numéro de notice : A2022-157 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.012 Date de publication en ligne : 24/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100365 [article]

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• Multi-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot (2021)  

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Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation / Kathrin Maier in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 1 - 18 Titre : Direct photogrammetry with multispectral imagery for UAV-based snow depth estimation Type de document : Article/Communication Auteurs : Kathrin Maier, Auteur ; Andrea Nascetti, Auteur ; Ward van Pelt, Auteur ; Gunhild Rosqvist, Auteur Année de publication : 2022 Article en page(s) : pp 1 - 18 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] bande infrarouge [Termes IGN] épaisseur [Termes IGN] erreur moyenne quadratique [Termes IGN] géoréférencement direct [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] manteau neigeux [Termes IGN] modèle numérique de surface [Termes IGN] photogrammétrie aérienne [Termes IGN] positionnement cinématique en temps réel [Termes IGN] qualité du modèle [Termes IGN] reconstruction 3D [Termes IGN] structure-from-motion [Termes IGN] Suède Résumé : (Auteur) More accurate snow quality predictions are needed to economically and socially support communities in a changing Arctic environment. This contrasts with the current availability of affordable and efficient snow monitoring methods. In this study, a novel approach is presented to determine spatial snow depth distribution in challenging alpine terrain that was tested during a field campaign performed in the Tarfala valley, Kebnekaise mountains, northern Sweden, in April 2019. The combination of a multispectral camera and an Unmanned Aerial Vehicle (UAV) was used to derive three-dimensional (3D) snow surface models via Structure from Motion (SfM) with direct georeferencing. The main advantage over conventional photogrammetric surveys is the utilization of accurate Real-Time Kinematic (RTK) positioning which enables direct georeferencing of the images, and therefore eliminates the need for ground control points. The proposed method is capable of producing high-resolution 3D snow-covered surface models (7 cm/pixel) of alpine areas up to eight hectares in a fast, reliable and affordable way. The test sites’ average snow depth was 160 cm with an average standard deviation of 78 cm. The overall Root-Mean-Square Errors (RMSE) of the snow depth range from 11.52 cm for data acquired in ideal surveying conditions to 41.03 cm in aggravated light and wind conditions. Results of this study suggest that the red components in the electromagnetic spectrum, i.e., the red, red edge, and near-infrared (NIR) band, contain the majority of information used in photogrammetric processing. The experiments highlighted a significant influence of the multi-spectral imagery on the quality of the final snow depth estimation as well as a strong potential to reduce processing times and computational resources by limiting the dimensionality of the imagery through the application of a Principal Component Analysis (PCA) before the photogrammetric 3D reconstruction. The proposed method is part of closing the scale gap between discrete point measurements and regional-scale remote sensing and complements large-scale remote sensing data and snow model output with an adequate validation source. Numéro de notice : A2022-066 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.020 Date de publication en ligne : 09/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99783 [article]

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PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 123 - 139 Titre : PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Scott Hensley, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 123 - 139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse discriminante [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] bande L [Termes IGN] données lidar [Termes IGN] forêt boréale [Termes IGN] forêt tropicale [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur de la végétation [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] polarimétrie radar [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réseau antagoniste génératif [Termes IGN] semis de points Résumé : (auteur) This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m). Numéro de notice : A2022-195 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.02.008 Date de publication en ligne : 17/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99962 [article]

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Calibrating GNSS phase biases with onboard observations of low earth orbit satellites / Xingxing Li in Journal of geodesy, vol 96 n° 2 (February 2022)  

Public [article]  inJournal of geodesy > vol 96 n° 2 (February 2022) . - n° 8 Titre : Calibrating GNSS phase biases with onboard observations of low earth orbit satellites Type de document : Article/Communication Auteurs : Xingxing Li, Auteur ; Jiaqi Wu, Auteur ; Xin Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 8 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] bande K [Termes IGN] erreur de phase [Termes IGN] erreur systématique [Termes IGN] étalonnage des données [Termes IGN] orbite basse [Termes IGN] phase GNSS [Termes IGN] positionnement ponctuel précis [Termes IGN] résolution d'ambiguïté Résumé : (auteur) recent years, numerous low earth orbit (LEO) satellites have been launched for different scientific tasks such as the Earth’s magnetic field, gravity recovering and ocean altimetry. The LEO satellites can cover the ocean area and are less affected by atmospheric delays and multipath errors, which provides new opportunities for calibrating the phase biases of the Global Navigation Satellite System (GNSS). In this contribution, we propose an alternative approach for uncalibrated phase delay (UPD) estimation by making full use of onboard observations of LEO satellites. Stable wide-lane (WL) and narrow-lane (NL) UPDs can be obtained from spaceborne GNSS observations and agree well with the UPD products derived from 106 IGS stations. To further verify the feasibility of the proposed method for UPD estimation, zero-difference (ZD) ambiguity resolution (AR) for precise point positioning (PPP) and LEO precise orbit determination (POD) are implemented. After applying the LEO-based UPDs, the averaged convergence time for PPP AR can be reduced to 15.2 min, with an improvement of 24% compared to float solutions. As for LEO AR, the fixing rates of WL and NL ambiguities exceed 98 and 92%, respectively. The accuracies of ambiguity-fixed orbits are validated by comparing with external satellite laser ranging (SLR) and K-band ranging (KBR) observations. Compared to float solutions, the standard deviations (STDs) of SLR residuals can be reduced by 8 ~ 43%, and the KBR residuals of 3.75 mm can be achieved for fixed solutions using LEO-based UPDs, with an improvement of 60%. Although the current UPD results derived from LEO satellites are slightly worse than those of ground-based UPD, it is anticipated that the performance of LEO-based UPD can be further improved in the near future with the rapidly increasing number of LEO satellites and the continuous refinements of the POD method. Numéro de notice : A2022-129 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.1007/s00190-022-01600-5 Date de publication en ligne : 31/01/2022 En ligne : https://doi.org/10.1007/s00190-022-01600-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99712 [article]

Use of remotely sensed data to estimate tree species diversity as an indicator of biodiversity in Blouberg Nature Reserve, South Africa / Mangana Rampheri in Geocarto international, vol 37 n° 2 ([15/01/2022])  

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Examining the integration of Landsat operational land imager with Sentinel-1 and vegetation indices in mapping southern yellow pines (Loblolly, Shortleaf, and Virginia pines) / Clement E. Akumu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 1 (January 2022)  

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Baseline-dependent clock offsets in VLBI data analysis / Hana Krásná in Journal of geodesy, vol 95 n° 12 (December 2021)  

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Multi-model estimation of forest canopy closure by using red edge bands based on Sentinel-2 images / Yiying Hua in Forests, vol 12 n° 12 (December 2021)  

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Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])  

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Downscaling MODIS spectral bands using deep learning / Rohit Mukherjee in GIScience and remote sensing, vol 58 n° 8 (2021)  

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Investigation of the landslides in Beylikdüzü-Esenyurt districts of Istanbul from InSAR and GNSS observations / Caglar Bayik in Natural Hazards, vol 109 n° 1 (October 2021)  

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Seawater Debye model function at L-band and its impact on salinity retrieval from Aquarius satellite data / Yiwen Zhou in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

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Coniferous and broad-leaved forest distinguishing using L-band polarimetric SAR data / Fang Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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Sentinel-1 sensitivity to soil moisture at high incidence angle and the impact on retrieval over seasonal crops / Davide Palmisano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

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