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Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network / Chen Chen in Remote sensing of environment, vol 270 (March 2022)
[article]
Titre : Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network Type de document : Article/Communication Auteurs : Chen Chen, Auteur ; Yi Ma, Auteur ; Guangbo Ren, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 112885 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] carte d'occupation du sol
[Termes IGN] carte thématique
[Termes IGN] image hyperspectrale
[Termes IGN] image Sentinel-MSI
[Termes IGN] littoral
[Termes IGN] marais salant
[Termes IGN] réseau antagoniste génératifRésumé : (auteur) Coastal wetlands are main components of the “blue carbon” ecosystems in coastal zones. Salt-marsh biomass is especially important regarding climate-change mitigation. Generating high precision biomass maps for evaluating the ecological functions of coastal wetlands is essential; however, conducting accurate biomass inversions with limited in situ observations from coastal wetlands is challenging. We propose a generative adversarial network with a constrained factor model (GAN-CF) for expanding limited in situ salt-marsh biomass observations. We used Sentinel-2 images and a deep belief network based on the conjugate gradient method (CG-DBN) for obtaining land-cover maps and the salt-marsh distribution (species: Phragmites australis, Suaeda glauca, Spartina alterniflora, and mixed species dominated by Tamarix chinensis) in the study area. This study bridges in situ hyperspectral and Sentinel-2 multispectral data by a satellite-band equivalent conversion model. The biomass and multispectral data derived from Sentinel-2 were used as input for the proposed GAN-CF model, which produced and constrained the generated samples based on the features (i.e., spectra, vegetation index, and biomass) of the in situ observations. Aboveground biomass (AGB) maps at 10-m spatial resolution were produced by constructing multiple linear regression models (MLRMs) based on the generated samples of each salt-marsh type using Sentinel-2 images. The quantity and richness of the generated samples improved the AGB estimations in the study area. The inversion accuracy of S. alterniflora was significantly improved (RMSE = 3.71 Mg/ha); the estimated AGB was strongly related to the in situ observations (R = 0.923). The estimated AGB was validated using in situ observations. The total amount of salt-marsh AGB in the study area in 2019 was estimated at 2.36 × 105 Mg, with 7.95 Mg/ha average. The salt-marsh biomass in decreasing order was as follows: P. australis (12.7 Mg/ha) > S. alterniflora (11.5 Mg/ha) > mixed species (8.97 Mg/ha) > S. glauca (2.18 Mg/ha). The salt-marsh area in decreasing order was as follows: S. glauca (10,410 ha) > P. australis (7320 ha) > mixed species (6740 ha) > S. alterniflora (5240 ha). By a feasibility analysis we estimated the biomass based on the Sentinel-2 data covering the Yellow River delta wetland in May, July, and September 2019 and the Jiaozhou Bay wetland in September 2019 by using the generated samples. The generated samples based on the 2013–2019 in situ observations constitute a salt-marsh biomass database, which can be useful for quantifying the regional carbon storage and ecological restoration monitoring. Numéro de notice : A2022-128 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112885 Date de publication en ligne : 07/01/2022 En ligne : https://doi.org/10.1016/j.rse.2021.112885 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99710
in Remote sensing of environment > vol 270 (March 2022) . - n° 112885[article]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)
[article]
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'imageRésumé : (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
in Remote sensing > vol 14 n° 5 (March-1 2022) . - n° 1272;[article]Deformation analysis: the modified GREDOD method / Mehmed Batilović in Geodetski vestnik, vol 66 n° 1 (March 2022)
[article]
Titre : Deformation analysis: the modified GREDOD method Type de document : Article/Communication Auteurs : Mehmed Batilović, Auteur ; Željko Kanović, Auteur ; Zoran Sušić, Auteur ; Marko Z. Marković, Auteur ; Vladimir Bulatović, Auteur Année de publication : 2022 Article en page(s) : pp 60 - 75 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Systèmes de référence et réseaux
[Termes IGN] algorithme génétique
[Termes IGN] déformation géométrique
[Termes IGN] méthode robuste
[Termes IGN] optimisation par essaim de particulesRésumé : (auteur) In this paper, a modified Generalised Robust Estimation of Deformation from Observation Differences (GREDOD) method is presented, based on the application of genetic algorithm (GA) and generalised particle swarm optimisation (GPSO) algorithm in solving the optimisation problem of this method, which is, in essence, a problem of determining the optimal datum of the displacement vector. The procedure of deformation analysis using this modification of the GREDOD method is demonstrated in the example of the two-dimensional geodetic network presented in numerous research and in which all observations and displacements were simulated. Using both algorithms, GA and GPSO, almost identical results of deformation analysis were obtained, except datum solutions of the displacement vector, which are completely different. These results differ only slightly from the results obtained using the methods of Hannover, Karlsruhe, Delft, Fredericton, München, Caspary, and the classical robust method. Numéro de notice : A2022-453 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2022.01.60-75 En ligne : https://dx.doi.org/10.15292/geodetski-vestnik.2022.01.60-75 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100984
in Geodetski vestnik > vol 66 n° 1 (March 2022) . - pp 60 - 75[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models / Lamin R. Mansaray in Geocarto international, vol 37 n° 5 ([01/03/2022])
[article]
Titre : Evaluating Sentinel-1A datasets for rice leaf area index estimation based on machine learning regression models Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Adam Sheka Kanu, Auteur ; Lingbo Yang, Auteur Année de publication : 2022 Article en page(s) : pp 1225 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage automatique
[Termes IGN] Chine
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image Sentinel-SAR
[Termes IGN] jeu de données localisées
[Termes IGN] Leaf Area Index
[Termes IGN] modèle de régression
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] polarisation
[Termes IGN] rizièreRésumé : (Auteur) Three Sentinel-1A datasets in vertical transmitted and horizontal received (VH) and vertical transmitted and vertical received (VV) polarisations, and the linear combination of VH and VV (VHVV) are evaluated for rice green leaf area index (LAI) estimation using four machine learning regression models [Support Vector Machine (SVM), k-Nearest Neighbour (k-NN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT)]. Results showed that for the entire growing season, VV outperformed VH, recording an R2 of 0.68 and an RMSE of 0.98 m2/m2 with the k-NN model. However, VHVV produced the most accurate estimates with GBDT (R2 of 0.82 and RMSE of 0.68 m2/m2), followed by that of VHVV with RF (R2 of 0.78 and RMSE of 0.90 m2/m2). Our findings have further confirmed that combining VH and VV data can achieve improved rice growth modelling, and that tree-based algorithms can better handle data dimensionality. Numéro de notice : A2022-274 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1773545 Date de publication en ligne : 05/06/2020 En ligne : https://doi.org/10.1080/10106049.2020.1773545 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100753
in Geocarto international > vol 37 n° 5 [01/03/2022] . - pp 1225 - 1236[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022051 RAB Revue Centre de documentation En réserve L003 Disponible Évaluation des apports de l’apprentissage profond au sein d’un service dédié à la numérisation du patrimoine / Maxime Mérizette in XYZ, n° 170 (mars 2022)
[article]
Titre : Évaluation des apports de l’apprentissage profond au sein d’un service dédié à la numérisation du patrimoine Type de document : Article/Communication Auteurs : Maxime Mérizette, Auteur Année de publication : 2022 Article en page(s) : pp 61 - 65 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage profond
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] jeu de données localisées
[Termes IGN] modélisation 3D du bâti BIM
[Termes IGN] qualité des données
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] segmentation d'image
[Termes IGN] semis de pointsRésumé : (Auteur) Les scanners laser terrestres permettent d’acquérir beaucoup de données tout en présentant une rapidité et une facilité d’acquisition. Mais ceci est terni par le manque d’automatisation des traitements de nuages de points. La segmentation de nuage de points, consistant à extraire les éléments constitutifs d’un nuage, pâtit notamment de ce manque. Ce travail de fin d’études d’ingénieur, mené chez Quarta, se concentre sur les apports de l’apprentissage profond pour la réalisation d’une segmentation de nuage de points. Elle se propose de lister les différentes méthodes d’apprentissage profond permettant de travailler sur les nuages de points et teste différents algorithmes permettant de traiter les nuages de points volumineux. Numéro de notice : A2022-226 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100192
in XYZ > n° 170 (mars 2022) . - pp 61 - 65[article]Réservation
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