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Termes IGN > sciences naturelles > physique > optique > optique physique > radiométrie > rayonnement électromagnétique > spectre électromagnétique > bande spectrale
bande spectraleSynonyme(s)canal spectral |
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Forest height estimation using a single-pass airborne L-band polarimetric and interferometric SAR system and tomographic techniques / Yue Huang in Remote sensing, Vol 13 n° 3 (February 2021)
[article]
Titre : Forest height estimation using a single-pass airborne L-band polarimetric and interferometric SAR system and tomographic techniques Type de document : Article/Communication Auteurs : Yue Huang, Auteur ; Qiaoping Zhang, Auteur ; Laurent Ferro-Famil, Auteur Année de publication : 2021 Article en page(s) : n° 487 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Alberta (Canada)
[Termes IGN] bande L
[Termes IGN] forêt boréale
[Termes IGN] hauteur des arbres
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] polarimétrie radar
[Termes IGN] surveillance forestière
[Termes IGN] tomographie radarRésumé : (auteur) This paper addresses forest height estimation for boreal forests at the test site of Edson in Alberta, Canada, using dual-baseline PolInSAR dataset measured by Intermap’s single-pass system. This particular dataset is acquired by using both ping-pong and non-ping-pong modes, which permit forming a dual-baseline TomoSAR configuration, i.e., an extreme configuration for tomographic processing. A tomographic approach, based on polarimetric Capon and MUSIC estimators, is proposed to estimate the elevation of tree top and of underlying ground, and hence forest height is estimated. The resulting forest DTM and DSM over the test site are validated against LiDAR-derived estimates, demonstrating the undeniable capability of the single-pass L-band PolInSAR system for forest monitoring. Numéro de notice : A2021-200 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13030487 Date de publication en ligne : 30/01/2021 En ligne : https://doi.org/10.3390/rs13030487 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97153
in Remote sensing > Vol 13 n° 3 (February 2021) . - n° 487[article]Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)
[article]
Titre : Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam Type de document : Article/Communication Auteurs : Vu Anh Tuan, Auteur ; Nguyen Hong Quang, Auteur ; le Thi Thu Hang, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 28 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande L
[Termes IGN] cartographie des risques
[Termes IGN] crue
[Termes IGN] image ALOS
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] Mekong (fleuve)
[Termes IGN] optimisation spatiale
[Termes IGN] surveillance hydrologique
[Termes IGN] Viet NamRésumé : (auteur) One major characteristic of floods is flood extent. Information on this characteristic is indispensable for flood monitoring. Recently, synthetic aperture radar (SAR) data have been increasing in quality and quantity. This allows more flood studies conducted over large areas regardless of cloud and weather conditions and provides advantages including clear surface water classification based on SAR scattering mechanisms for low values (open water) and high values (inundated vegetation, etc.). However, challenges remain due to sources of uncertainties, such as atmospheric disturbances and vegetation masking parts of water surfaces. Therefore, in this study, we aim to optimize flood mapping processes on flooded vegetation that generated high-value pixels based on a SAR scattering mechanism called double bounce that classifies vegetative flooded water in L-band SAR images. This optimization is nearly impossible using Sentinel-1 scenes. Backscattering of time-series Sentinel-1 and ALOS-2 images acquired for the 2018 and 2019 flood season was analysed, thresholded and hybridized for flood mapping of a study site in the Tam Nong district of the Dong Thap Province of Vietnam. We found that the accuracy of SAR flood maps was improved compared to ground truth data when the SAR-extracted vegetative-flooded plains were considered flooded. Numéro de notice : A2021-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1859340 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1859340 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97015
in European journal of remote sensing > vol 54 n° 1 (2021) . - pp 13 - 28[article]Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
[article]
Titre : Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning Type de document : Article/Communication Auteurs : Maryam Pourshamsi, Auteur ; Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage automatique
[Termes IGN] bande L
[Termes IGN] canopée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données polarimétriques
[Termes IGN] forêt tropicale
[Termes IGN] Gabon
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] image radar moirée
[Termes IGN] Rotation Forest classification
[Termes IGN] semis de pointsRésumé : (auteur) Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. Numéro de notice : A2021-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.008 Date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96846
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 79 - 94[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Exclu du prêt Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)
Titre : Deep learning for wildfire progression monitoring using SAR and optical satellite image time series Type de document : Thèse/HDR Auteurs : Puzhao Zhang, Auteur Editeur : Stockholm : Royal Institute of Technology Année de publication : 2021 Importance : 100 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-91-7873-935-6 Note générale : bibliographie
Doctoral Thesis in GeoinformaticsLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alberta (Canada)
[Termes IGN] apprentissage profond
[Termes IGN] bande C
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] détection de changement
[Termes IGN] gestion des risques
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] incendie de forêt
[Termes IGN] série temporelle
[Termes IGN] surveillance forestière
[Termes IGN] Sydney (Nouvelle-Galles du Sud)
[Termes IGN] zone sinistréeRésumé : (auteur) Wildfires have coexisted with human societies for more than 350 million years, always playing an important role in affecting the Earth's surface and climate. Across the globe, wildfires are becoming larger, more frequent, and longer-duration, and tend to be more destructive both in lives lost and economic costs, because of climate change and human activities. To reduce the damages from such destructive wildfires, it is critical to track wildfire progressions in near real-time, or even real-time. Satellite remote sensing enables cost-effective, accurate, and timely monitoring on the wildfire progressions over vast geographic areas. The free availability of global coverage Landsat-8 and Sentinel-1/2 data opens the new era for global land surface monitoring, providing an opportunity to analyze wildfire impacts around the globe. The advances in both cloud computing and deep learning empower the automatic interpretation of spatio-temporal remote sensing big data on a large scale. The overall objective of this thesis is to investigate the potential of modern medium resolution earth observation data, especially Sentinel-1 C-Band synthetic aperture radar (SAR) data, in wildfire monitoring and develop operational and effective approaches for real-world applications. This thesis systematically analyzes the physical basis of earth observation data for wildfire applications, and critically reviews the available wildfire burned area mapping methods in terms of satellite data, such as SAR, optical, and SAR-Optical fusion. Taking into account its great power in learning useful representations, deep learning is adopted as the main tool to extract wildfire-induced changes from SAR and optical image time series. On a regional scale, this thesis has conducted the following four fundamental studies that may have the potential to further pave the way for achieving larger scale or even global wildfire monitoring applications. To avoid manual selection of temporal indices and to highlight wildfire-induced changes in burned areas, we proposed an implicit radar convolutional burn index (RCBI), with which we assessed the roles of Sentinel-1 C-Band SAR intensity and phase in SAR-based burned area mapping. The experimental results show that RCBI is more effective than the conventional log-ratio differencing approach in detecting burned areas. Though VV intensity itself may perform poorly, the accuracy can be significantly improved when phase information is integrated using Interferometric SAR (InSAR). On the other hand, VV intensity also shows the potential to improve VH intensity-based detection results with RCBI. By exploiting VH and VV intensity together, the proposed RCBI achieved an overall mapping accuracy of 94.68% and 94.17% on the 2017 Thomas Fire and the 2018 Carr Fire. For the scenario of near real-time application, we investigated and demonstrated the potential Sentinel-1 SAR time series for wildfire progression monitoring with Convolutional Neural Networks (CNN). In this study, the available pre-fire SAR time series were exploited to compute temporal average and standard deviation for characterizing SAR backscatter behaviors over time and highlighting the changes with kMap. Trained with binarized kMap time series in a progression-wise manner, CNN showed good capability in detecting wildfire burned areas and capturing temporal progressions as demonstrated on three large and impactful wildfires with various topographic conditions. Compared to the pseudo masks (binarized kMap), CNN-based framework brought an 0.18 improvement in F1 score on the 2018 Camp Fire, and 0.23 on the 2019 Chuckegg Creek Fire. The experimental results demonstrated that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals. For continuous wildfire progression mapping, we proposed a novel framework of learning U-Net without forgetting in a near real-time manner. By imposing a temporal consistency restriction on the network response, Learning without Forgetting (LwF) allows the U-Net to learn new capabilities for better handling with newly incoming data, and simultaneously keep its existing capabilities learned before. Unlike the continuous joint training (CJT) with all available historical data, LwF makes U-Net learning not dependent on the historical training data any more. To improve the quality of SAR-based pseudo progression masks, we accumulated the burned areas detected by optical data acquired prior to SAR observations. The experimental results demonstrated that LwF has the potential to match CJT in terms of the agreement between SAR-based results and optical-based ground truth, achieving a F1 score of 0.8423 on the Sydney Fire (2019-2020) and 0.7807 on the Chuckegg Creek Fire (2019). We also found that the SAR cross-polarization ratio (VH/VV) can be very useful in highlighting burned areas when VH and VV have diverse temporal change behaviors. SAR-based change detection often suffers from the variability of the surrounding background noise, we proposed a Total Variation (TV)-regularized U-Net model to relieve the influence of SAR-based noisy masks. Considering the small size of labeled wildfire data, transfer learning was adopted to fine-tune U-Net from pre-trained weights based on the past wildfire data. We quantified the effects of TV regularization on increasing the connectivity of SAR-based areas, and found that TV-regularized U-Net can significantly increase the burned area mapping accuracy, bringing an improvement of 0.0338 in F1 score and 0.0386 in IoU score on the validation set. With TV regularization, U-Net trained with noisy SAR masks achieved the highest F1 (0.6904) and IoU (0.5295), while U-Net trained with optical reference mask achieved the highest F1 (0.7529) and IoU (0.6054) score without TV regularization. When applied on wildfire progression mapping, TV-regularized U-Net also worked significantly better than vanilla U-Net with the supervision of noisy SAR-based masks, visually comparable to optical mask-based results. On the regional scale, we demonstrated the effectiveness of deep learning on SAR-based and SAR-optical fusion based wildfire progression mapping. To scale up deep learning models and make them globally applicable, large-scale globally distributed data is needed. Considering the scarcity of labelled data in the field of remote sensing, weakly/self-supervised learning will be our main research directions to go in the near future. Note de contenu : 1- Introduction
2- Literature review
3- Study areas and data
4- Metodology
5- Results and discussionNuméro de notice : 28309 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Geomatics : RTK Stockholm : 2021 DOI : sans En ligne : http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A1557429 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98130 Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations / Adnane Chakir (2021)
Titre : Diurnal cycles of C-band temporal coherence and backscattering coefficient over an olive orchard in a semi-arid area: Comparison of in situ and Sentinel-1 radar observations Type de document : Article/Communication Auteurs : Adnane Chakir , Auteur ; Pierre-Louis Frison , Auteur ; Saïd Khabba, Auteur ; Jamal Ezzahar, Auteur ; Ludovic Villard, Auteur ; Pascal Fanise, Auteur ; Nadia Ouaadi, Auteur ; V. Ledantec, Auteur ; Lionel Jarlan, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2021 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : IGARSS 2021, IEEE International Geoscience And Remote Sensing Symposium 11/07/2021 16/07/2021 Bruxelles Belgique Proceedings IEEE Importance : pp 3801 - 3804 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] cohérence temporelle
[Termes IGN] image Sentinel-SAR
[Termes IGN] Maroc
[Termes IGN] Olea europaea
[Termes IGN] vergerRésumé : (auteur) C-band radar remote sensing is a suitable tool for monitoring agricultural areas on a large scale, providing access to information on vegetation such as plant biomass, or on the surface water content of the soil. Recent studies suggest that the water state and the physiological functioning of trees influence radar response leading to marked daily profiles of both radar backscattering coefficient and temporal coherence. The objective of this paper is to make a preliminary comparison between the temporal evolution of Sentinel-1 radar data and in situ radar measurements over a Mediterranean olive orchard located in Morocco. In situ radar data consist in quad polarizations measurements realized from a 20m high tower, every 15 minutes, for the period extending from May 2019 to October 2020. Numéro de notice : C2021-051 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS47720.2021.9553129 Date de publication en ligne : 12/10/2021 En ligne : https://doi.org/10.1109/IGARSS47720.2021.9553129 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99415 Diurnal cycles of C-band temporal coherence and backscattering coefficient over a wheat field in a semi-arid area / Nadia Ouaadi (2021)PermalinkFlood mapping from radar remote sensing using automated image classification techniques / Lisa Landuyt (2021)PermalinkPermalinkPermalinkSuper-resolution of VIIRS-measured ocean color products using deep convolutional neural network / Xiaoming Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)PermalinkUnmixing-based Sentinel-2 downscaling for urban land cover mapping / Fei Xu in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)PermalinkMonitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas / Nadia Ouaadi in Remote sensing of environment, Vol 251 (15 December 2020)PermalinkIntegrated Kalman filter of accurate ranging and tracking with wideband radar / Shaopeng Wei in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)PermalinkL’Ultra Wideband, un système de positionnement topographique sans satellite / Joël Van Cranenbroeck in XYZ, n° 165 (décembre 2020)PermalinkCombination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)Permalink