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Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3040 - 3053 Titre : Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data Type de document : Article/Communication Auteurs : Xiao-Ming Li, Auteur ; Yan Sun, Auteur ; Qiang Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 3040 - 3053 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] Arctique, océan [Termes IGN] classification non dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données d'apprentissage [Termes IGN] entropie [Termes IGN] extraction de traits caractéristiques [Termes IGN] glace de mer [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] matrice de co-occurrence [Termes IGN] niveau de gris (image) [Termes IGN] polarisation croisée [Termes IGN] rétrodiffusion [Termes IGN] texture Résumé : (auteur) In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical–horizontal (VH) or horizontal–vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%–95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%. Numéro de notice : A2021-284 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007789 date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007789 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97392 [article]

Principes fondamentaux de la rétrodiffusion radar / Jean-Paul Rudant (2021)  

Public Titre : Principes fondamentaux de la rétrodiffusion radar Type de document : Guide/Manuel Auteurs : Jean-Paul Rudant , Auteur Editeur : Jena [Allemagne] : EO College Année de publication : 2021 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] image radar [Termes IGN] rétrodiffusion [Termes IGN] télédétection en hyperfréquence Résumé : (auteur) Ce cours vous informe sur les paramètres les plus pertinents qui ont une influence sur le signal radar rétrodiffusé. Vous comprendrez quels rôles jouent les caractéristiques d’un système radar et les propriétés de la surface terrestre dans la réponse radar. Ainsi, vous pourrez interpréter les images. Note de contenu : Le cours se compose de quatre leçons et porte sur les sujets suivants : - Introduction au concept de rétrodiffusion radar - Influence sur la réponse radar des paramètres liés au capteurs - Influence sur la réponse radar des paramètres liés à la géométrie de la surface terrestre, en particulier du relief - Influence des paramètres diélectriques sur la réponse radar Numéro de notice : 17644 Affiliation des auteurs : UGE-LaSTIG (2020- ) Thématique : IMAGERIE Nature : Manuel de cours nature-HAL : Cours DOI : sans En ligne : https://eo-college.org/courses/principes-de-la-retrodiffusion-radar/#learndash-c [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97621

Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data / Yaotong Cai in International journal of applied Earth observation and geoinformation, vol 92 (October 2020)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 92 (October 2020) . - n° 102164 Titre : Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data Type de document : Article/Communication Auteurs : Yaotong Cai, Auteur ; Xinyu Li, Auteur ; Meng Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 102164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] algorithme de généralisation [Termes IGN] analyse d'image orientée objet [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] filtre de déchatoiement [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] modélisation spatio-temporelle [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] prairie [Termes IGN] rétrodiffusion [Termes IGN] série temporelle [Termes IGN] zone humide Résumé : (auteur) Wetland ecosystems have experienced dramatic challenges in the past few decades due to natural and human factors. Wetland maps are essential for the conservation and management of terrestrial ecosystems. This study is to obtain an accurate wetland map using an object-based stacked generalization (Stacking) method on the basis of multi-temporal Sentinel-1 and Sentinel-2 data. Firstly, the Robust Adaptive Spatial Temporal Fusion Model (RASTFM) is used to get time series Sentinel-2 NDVI, from which the vegetation phenology variables are derived by the threshold method. Subsequently, both vertical transmit-vertical receive (VV) and vertical transmit-horizontal receive (VH) polarization backscatters (σ0 VV, σ0 VH) are obtained using the time series Sentinel-1 images. Speckle noise inherent in SAR data, resulting in over-segmentation or under-segmentation, can affect image segmentation and degrade the accuracies of wetland classification. Therefore, we segment Sentinel-2 multispectral images to delineate meaningful objects in this study. Then, in order to reduce data redundancy and computation time, we analyze the optimal feature combination using the Sentinel-2 multispectral images, Sentinel-2 NDVI time series, phenological variables and other vegetation index derived from Sentinel-2 multispectral images, as well as time series Sentinel-1 backscatters at the object level. Finally, the stacked generalization algorithm is utilized to extract the wetland information based on the optimal feature combination in the Dongting Lake wetland. The overall accuracy and Kappa coefficient of the object-based stacked generalization method are 92.46% and 0.92, which are 3.88% and 0.04 higher than that using the pixel-based method. Moreover, the object-based stacked generalization algorithm is superior to single classifiers in classifying vegetation of high heterogeneity areas. Numéro de notice : A2020-748 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2020.102164 date de publication en ligne : 07/06/2020 En ligne : https://doi.org/10.1016/j.jag.2020.102164 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96398 [article]

Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 / Helena Bergstedt in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6008 - 6019 Titre : Deriving a frozen area fraction from Metop ASCAT backscatter based on Sentinel-1 Type de document : Article/Communication Auteurs : Helena Bergstedt, Auteur ; Annett Bartsch, Auteur ; Anton Neureiter, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6008 - 6019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Autriche [Termes IGN] bande C [Termes IGN] courbe de Pearson [Termes IGN] dégel [Termes IGN] Finlande [Termes IGN] fonte des glaces [Termes IGN] hétérogénéité spatiale [Termes IGN] image MetOp-ASCAT [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] pergélisol [Termes IGN] rétrodiffusion [Termes IGN] série temporelle [Termes IGN] télédétection en hyperfréquence [Termes IGN] température au sol Résumé : (auteur) Surface state data derived from spaceborne microwave sensors with suitable temporal sampling are to date only available in low spatial resolution (25–50 km). Current approaches do not adequately resolve spatial heterogeneity in landscape-scale freeze–thaw processes. We propose to derive a frozen fraction instead of binary freeze–thaw information. This introduces the possibility to monitor the gradual freezing and thawing of complex landscapes. Frozen fractions were retrieved from Advanced Scatterometer (ASCAT, C-band) backscatter on a 12.5-km grid for three sites in noncontinuous permafrost areas in northern Finland and the Austrian Alps. To calibrate the retrieval approach, frozen fractions based on Sentinel-1 synthetic aperture radar (SAR, C-band) were derived for all sites and compared to ASCAT backscatter. We found strong relationships for ASCAT backscatter with Sentinel-1 derived frozen fractions (Pearson correlations of −0.85 to −0.96) for the sites in northern Finland and less strong relationships for the Alpine site (Pearson correlations −0.579 and −0.611, including and excluding forested areas). Applying the derived linear relationships, predicted frozen fractions using ASCAT backscatter values showed root mean square error (RMSE) values between 7.26% and 16.87% when compared with Sentinel-1 frozen fractions. The validation of the Sentinel-1 derived freeze–thaw classifications showed high accuracy when compared to in situ near-surface soil temperature (84.7%–94%). Results are discussed with regard to landscape type, differences between spring and autumn, and gridding. This article serves as a proof of concept, showcasing the possibility to derive frozen fraction from coarse spatial resolution scatterometer time series to improve the representation of spatial heterogeneity in landscape-scale surface state. Numéro de notice : A2020-525 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2967364 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2967364 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95702 [article]

Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms / Gustavo H.X. Shiroma in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 754 - 3777 Titre : Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms Type de document : Article/Communication Auteurs : Gustavo H.X. Shiroma, Auteur ; Marco Lavalle, Auteur Année de publication : 2020 Article en page(s) : pp 754 - 3777 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bande L [Termes IGN] décomposition de Gauss [Termes IGN] Gabon [Termes IGN] histogramme [Termes IGN] image captée par drone [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation 3D [Termes IGN] polarimétrie radar [Termes IGN] rétrodiffusion [Termes IGN] structure de la végétation Résumé : (auteur) This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopé National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH−VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements. Numéro de notice : A2020-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2956989 date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2956989 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95099 [article]

Potential of texture from SAR tomographic images for forest aboveground biomass estimation / Zhanmang Liao in International journal of applied Earth observation and geoinformation, vol 88 (June 2020)  

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Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

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Integration of corner reflectors for the monitoring of mountain glacier areas with Sentinel-1 time series / Matthias Jauvin in Remote sensing, vol 11 n° 8 (August 2019)  

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Imagerie radar en ondes millimétriques appliquée à la viticulture / Dominique Henry (2018)  

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Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

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A time-series approach to estimating soil moisture from vegetated surfaces using L-band radar backscatter / Jeffrey D. Ouellette in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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Mise en place d'une méthode semi-automatique de cartographie de l'occupation des sols à partir d'images SAR polarimétriques / Monique Moine in Revue Française de Photogrammétrie et de Télédétection, n° 215 (mai - août 2017)

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Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E / Jinyang Du in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

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Understanding the effects of ALS pulse density for metric retrieval across diverse forest types / Phil Wilkes in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)

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Non-invasive forest litter characterization using full-wave inversion of microwave radar data / Frédéric André in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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