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Python software to transform GPS SNR wave phases to volumetric water content / Angel Martín in GPS solutions, vol 26 n° 1 (January 2022)
[article]
Titre : Python software to transform GPS SNR wave phases to volumetric water content Type de document : Article/Communication Auteurs : Angel Martín, Auteur ; Ana Belén Anquela, Auteur ; Sara Ibáñez, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale
[Termes IGN] humidité du sol
[Termes IGN] phase
[Termes IGN] Python (langage de programmation)
[Termes IGN] rapport signal sur bruit
[Termes IGN] réflectométrie par GNSS
[Termes IGN] signal GPS
[Termes IGN] teneur en vapeur d'eauRésumé : (auteur) The global navigation satellite system interferometric reflectometry is often used to extract information about the environment surrounding the antenna. One of the most important applications is soil moisture monitoring. This manuscript presents the main ideas and implementation decisions needed to write the Python code to transform the derived phase of the interferometric GPS waves, obtained from signal-to-noise ratio data continuously observed during a period of several weeks (or months), to volumetric water content. The main goal of the manuscript is to share the software with the scientific community to help users in the GPS-IR computation. Numéro de notice : A2022-004 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-021-01190-3 Date de publication en ligne : 27/10/2021 En ligne : https://doi.org/10.1007/s10291-021-01190-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98919
in GPS solutions > vol 26 n° 1 (January 2022) . - n° 7[article]Le radar révèle des montagnes cachées / Laurent Polidori in Géomètre, n° 2198 (janvier 2022)
[article]
Titre : Le radar révèle des montagnes cachées Type de document : Article/Communication Auteurs : Laurent Polidori, Auteur Année de publication : 2022 Article en page(s) : pp 17 - 17 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] altimétrie satellitaire par radar
[Termes IGN] Biomass
[Termes IGN] longueur d'onde
[Termes IGN] représentation du relief
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] traitement d'image radarRésumé : (Auteur) Un radar embarqué sur satellite peut voir des reliefs cachés par l’eau, la forêt, le sable ou la glace. Numéro de notice : A2022-060 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtSansCL DOI : sans Date de publication en ligne : 01/01/2022 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99401
in Géomètre > n° 2198 (janvier 2022) . - pp 17 - 17[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 063-2022011 RAB Revue Centre de documentation En réserve L003 Disponible Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network / Yajie Shi in International Journal of Remote Sensing IJRS, vol 42 n° 23 (1-10 December 2021)
[article]
Titre : Improving soil moisture retrieval from GNSS-interferometric reflectometry: parameters optimization and data fusion via neural network Type de document : Article/Communication Auteurs : Yajie Shi, Auteur ; Chao Ren, Auteur ; Zhiheng Yan, Auteur ; Jianmin Lai, Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] fusion de données
[Termes IGN] humidité du sol
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réflectométrie par GNSS
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Soil moisture is a vital surface physical quantity in studying the earth’s ecology. It plays a crucial role in the hydrological cycle, crop yield estimation, and ecological monitoring. Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology inversion to obtain high accuracy soil moisture is a hot topic of current research. However, due to the limited available sites, it’s difficult to obtain an extensive and continuous range of soil moisture based on this technique. It is necessary to build algorithms for encryption based on known sites’ data, combined with the corresponding geographic environmental elements. This paper extracted the surface environmental factors affecting soil moisture using high-precision optical remote sensing images. The contribution of each surface environmental element to the soil moisture inversion was analysed using back propagation (BP) neural network optimized by the genetic algorithm (GA). Based on this, ten surface environmental elements (latitude and longitude information, precipitation, temperature, land cover type, normalized difference vegetation index (NDVI), elevation, slope, slope direction, and shading) were identified as critical factors, and a multi-data fusion soil moisture inversion model was constructed. The results showed that the constructed model could better describe the relationship between soil moisture and these elements, and the Pearson correlation coefficient R reached 0.8724, and the RMSE was 0.0346 cm3 cm−3. GNSS-IR technology provides an effective technical means for inversing soil moisture over a large area with high spatial and temporal resolution. Numéro de notice : A2021-786 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2021.1988186 Date de publication en ligne : 24/10/2021 En ligne : https://doi.org/10.1080/01431161.2021.1988186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98972
in International Journal of Remote Sensing IJRS > vol 42 n° 23 (1-10 December 2021)[article]Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation / Hamid Jafarzadeh in Remote sensing, vol 13 n° 21 (November-1 2021)
[article]
Titre : Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation Type de document : Article/Communication Auteurs : Hamid Jafarzadeh, Auteur ; Masoud Mahdianpari, Auteur ; Eric Gill, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 4405 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] apprentissage automatique
[Termes IGN] arbre de décision
[Termes IGN] boosting adapté
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données polarimétriques
[Termes IGN] ensachage
[Termes IGN] Extreme Gradient Machine
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image ROSISRésumé : (auteur) In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior results compared to other commonly used ML algorithms. Despite their popularity within the computer science community, they have not yet been well examined in detail in the field of Earth Observation (EO) for satellite image classification. As such, this study investigates the capability of different EL algorithms, generally known as bagging and boosting algorithms, including Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF), for the classification of Remote Sensing (RS) data. In particular, different classification scenarios were designed to compare the performance of these algorithms on three different types of RS data, namely high-resolution multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data. Moreover, the Decision Tree (DT) single classifier, as a base classifier, is considered to evaluate the classification’s accuracy. The experimental results demonstrated that the RF and XGBoost methods for the multispectral image, the LightGBM and XGBoost methods for hyperspectral data, and the XGBoost and RF algorithms for PolSAR data produced higher classification accuracies compared to other ML techniques. This demonstrates the great capability of the XGBoost method for the classification of different types of RS data. Numéro de notice : A2021-823 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13214405 Date de publication en ligne : 02/11/2021 En ligne : https://doi.org/10.3390/rs13214405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98938
in Remote sensing > vol 13 n° 21 (November-1 2021) . - n° 4405[article]Land subsidence in Beijing’s sub-administrative center and its relationship with urban expansion inferred from Sentinel-1/2 observations / Jin Cao in Canadian journal of remote sensing, vol 47 n° 6 ([01/11/2021])
[article]
Titre : Land subsidence in Beijing’s sub-administrative center and its relationship with urban expansion inferred from Sentinel-1/2 observations Titre original : Affaissement du sol dans le centre sous administratif de Beijing et sa relation avec l’expansion urbaine déduits des observations de Sentinel-1/2 Type de document : Article/Communication Auteurs : Jin Cao, Auteur ; Huili Gong, Auteur ; Beibei Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 802 - 817 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] croissance urbaine
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Pékin (Chine)
[Termes IGN] subsidenceRésumé : (auteur) Beijing’s Sub-Administrative Center (BSAC) is located in the South-eastern Beijing Plain, which exhibits severe subsidence. The rapid urban expansion in recent years has aggravated land subsidence and threatens the safe operation of Beijing. First, this study applied the persistent scatterer-interferometric synthetic aperture radar (PS-InSAR) to extract BSAC subsidence time series data. Second, combined with the index-based built-up index (IBI), expansion intensity index (EII), and expansion gradient index (EGI), the spatiotemporal characteristics of urban expansion were retrieved from optical data. Finally, we examined the urban expansion effects on land subsidence at the regional and single-building scales. The results showed that the maximum subsidence velocity in the BSAC reached 121 mm/year from 2015 to 2018, and the urban construction land area increased by 22%. At the regional scale, there existed a positive correlation between land subsidence and EGI or EII. This indicated that urban expansion had a certain impact on land subsidence. Therefore, we further explored the relationship between construction and land subsidence at the single-building scale. The engineering construction effects on land subsidence were divided into three periods, namely, rapid settlement, rebound, and stable periods. Although construction had a significant influence on land subsidence, it did not cause subsidence mutation. Numéro de notice : A2021-955 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1080/07038992.2021.1964944 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.1080/07038992.2021.1964944 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99981
in Canadian journal of remote sensing > vol 47 n° 6 [01/11/2021] . - pp 802 - 817[article]Persistent scatterer interferometry for Pettimudi (India) landslide monitoring using Sentinel-1A images / Hari Shankar in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 11 (November 2021)PermalinkEvaluation of methods for connecting InSAR to a terrestrial reference frame in the Latrobe Valley, Australia / P.J. Johnston in Journal of geodesy, vol 95 n° 10 (October 2021)PermalinkInvestigation 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)PermalinkOrbit error removal in InSAR/MTInSAR with a patch-based polynomial model / Yanan Du in International journal of applied Earth observation and geoinformation, vol 102 (October 2021)PermalinkConiferous 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)PermalinkEstimation of surface deformation due to Pasni earthquake using RADAR interferometry / Muhammad Ali in Geocarto international, vol 36 n° 14 ([01/08/2021])PermalinkUnsupervised denoising for satellite imagery using wavelet directional cycleGAN / Shaoyang Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)PermalinkTen years of Lake Taupō surface height estimates using the GNSS interferometric reflectometry / Lucas D. Holden in Journal of geodesy, vol 95 n° 7 (July 2021)PermalinkGlacier elevation change in the Western Qilian mountains as observed by TerraSAR-X/TanDEM-X images / Qibing Zhang in Geocarto international, vol 36 n° 12 ([01/07/2021])PermalinkForest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)Permalink