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Termes IGN > imagerie > image radar > image radar moirée
image radar moiréeSynonyme(s)Interferogramme ;image SAR ;Image rso ;Image radar interférométrique Image par radar à antenne synthétiqueVoir aussi
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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])
[article]
Titre : Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data Type de document : Article/Communication Auteurs : Thota Sivasankar, Auteur ; Dheeraj Kumar, Auteur ; Hari Shanker Srivastava, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 905 - 915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] coefficient de corrélation
[Termes IGN] image radar moirée
[Termes IGN] image Risat-1
[Termes IGN] indice foliaire
[Termes IGN] polarisation
[Termes IGN] régression non linéaire
[Termes IGN] rétrodiffusion
[Termes IGN] séparateur à vaste marge
[Termes IGN] surveillance de la végétationRésumé : (auteur) Leaf Area Index (LAI) is a key parameter to characterize the canopy–atmosphere interface, where most of the energy fluxes exchange. Space-borne satellite images have shown their relevance for various applications including LAI retrieval over large areas. Although optical data have been used for this purpose in previous studies, the constraints to acquire optical data during extreme weather conditions due to the presence of clouds, haze, smoke etc. hinders its use for uninterrupted monitoring. This study aims to analyze the relationships of C-band RISAT-1 hybrid polarized SAR data (σ˚RH and σ˚RV) with wheat LAI. The results have shown the correlation coefficient (|r|) of 0.57 and 0.73 for RH and RV backscatter, respectively, using non-linear regression approach. It is also observed that the accuracy of LAI retrieval has been significantly improved with |r| and RMSE of 0.81 and 0.54 (m2/m2), respectively, by considering both RH and RV backscatter as inputs for support vector machine-based model. Numéro de notice : A2020-341 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2019.1566404 Date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566404 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95219
in Geocarto international > Vol 35 n° 8 [01/06/2020] . - pp 905 - 915[article]Fusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method / Yuedong Wang in Journal of geodesy, vol 94 n° 5 (May 2020)
[article]
Titre : Fusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method Type de document : Article/Communication Auteurs : Yuedong Wang, Auteur ; Zefa Yang, Auteur ; Zhiwei Li, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse des risques
[Termes IGN] Chine
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données localisées 3D
[Termes IGN] données polarimétriques
[Termes IGN] échantillonnage de données
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] méthode des moindres carrés
[Termes IGN] mine de charbon
[Termes IGN] série temporelle
[Termes IGN] surveillance géologiqueRésumé : (auteur) Interferometric synthetic aperture radar (InSAR) technology can be used to observe high spatial resolution one-dimensional (1-D) deformation along the line-of-sight direction from a single-track synthetic aperture radar (SAR) dataset. With the aid of multi-track InSAR data or a prior model, InSAR can be extended to infer 3-D deformation information, but the temporal resolution is generally limited. This paper presents an InSAR-based method to retrieve high spatio-temporal resolution 3-D displacements over mining areas (hereafter referred to as the MTI-based method). The core idea of the proposed method is to enhance the temporal resolution of the time-series 3-D displacement estimates by fusing multi-track InSAR observations and a prior model. Firstly, we retrieve high spatial resolution 3-D mining displacements from single-track InSAR 1-D deformation observations, with the assistance of the prior deformation model. By applying this approach to multi-track InSAR data over the same area, we obtain much denser 3-D mining displacement samples in time than those derived from a single-track InSAR dataset. Secondly, we propose a generalized weighted least-squares method to integrate the denser 3-D displacement samples, to solve the high temporal resolution 3-D mining displacements, in which the rank deficiency needs to be tackled. Finally, time-series 3-D mining displacements at the chronological dates of all the available multi-track SAR images are estimated. The Yungang coal mining area of China was selected to test the proposed method using two adjacent-track ALOS PALSAR-1 datasets. Compared with the single-track InSAR-derived results, the proposed method not only significantly improves the temporal resolution of the monitoring results by 42.6%, obtaining more detailed 3-D displacements, but it also provides important data support for understanding and modeling the distinctive kinematics of mining deformation and assessing mining-related geohazards. What is more, the core idea of the proposed method will be beneficial to high spatio-temporal resolution 3-D deformation estimation in other geophysical processes. Numéro de notice : A2020-239 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01374-8 Date de publication en ligne : 23/04/2020 En ligne : https://doi.org/10.1007/s00190-020-01374-8 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94992
in Journal of geodesy > vol 94 n° 5 (May 2020)[article]Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])
[article]
Titre : Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping Type de document : Article/Communication Auteurs : Kristofer Lasko, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Asie du sud-est
[Termes IGN] bande C
[Termes IGN] carte de la végétation
[Termes IGN] cartographie des risques
[Termes IGN] dynamique de la végétation
[Termes IGN] image Aqua-MODIS
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] incendie de forêt
[Termes IGN] incertitude temporelle
[Termes IGN] Laos
[Termes IGN] qualité de l'air
[Termes IGN] Thaïlande
[Termes IGN] zone sinistréeRésumé : (auteur) Wildland fires result in a unique signal detectable by multispectral remote sensing and synthetic aperture radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre- and post-burn MODIS observations result in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A1 BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5 d, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies and monitoring post-fire vegetation dynamics. Numéro de notice : A2020-226 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1608592 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/https://doi.org/10.1080/10106049.2019.1608592 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94948
in Geocarto international > vol 35 n° 6 [01/05/2020][article]Region level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)
[article]
Titre : Region level SAR image classification using deep features and spatial constraints Type de document : Article/Communication Auteurs : Anjun Zhang, Auteur ; Xuezhi Yang, Auteur ; Shuai Fang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 36-48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] carte de confiance
[Termes IGN] champ aléatoire de Markov
[Termes IGN] chatoiement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image radar moirée
[Termes IGN] lissage de données
[Termes IGN] modélisation spatiale
[Termes IGN] précision de la classification
[Termes IGN] superpixelRésumé : (auteur) The region-level SAR image classification algorithms which combine CNN (Convolutional Neural Networks) with super-pixel have been proposed to enhance the classification accuracy compared with the pixel-level algorithms. However, the spatial constraints between the super-pixel regions are not considered, which may limit the performance of these algorithms. To address this problem, an RCC-MRF (RCC, Region Category Confidence-degree) and CNN based region-level SAR image classification algorithm which explores the deep features extracted by CNN and the spatial constraints between super-pixel regions is proposed in this paper. The initial labels of super-pixel regions are obtained using a voting strategy based on the predicted labels CNN. The unary energy function of RCC-MRF is designed to find the category that a region most probably belongs to by using the RCC term which is constructed based on the probability distributions over all categories of pixels predicted by CNN. The binary energy function of RCC-MRF explores the spatial constraints between the adjacent super-pixel regions. In our proposed algorithm, the pixel-level misclassifications can be reduced by the smoothing within regions and the region-level misclassifications will be rectified by minimizing the energy function of RCC-MRF. Experiments have been done on simulated and real SAR images to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm notably outperforms the other CNN-based region-level SAR image classification algorithms. Numéro de notice : A2020-136 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.001 Date de publication en ligne : 07/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94752
in ISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 36-48[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020051 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Saliency-guided single shot multibox detector for target detection in SAR images / Lan Du in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
[article]
Titre : Saliency-guided single shot multibox detector for target detection in SAR images Type de document : Article/Communication Auteurs : Lan Du, Auteur ; Lu Li, Auteur ; Di Wei, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3366 - 3376 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] fusion de données
[Termes IGN] image radar moirée
[Termes IGN] saillanceRésumé : (auteur) The single shot multibox detector (SSD), a proposal-free method based on convolutional neural network (CNN), has recently been proposed for target detection and has found applications in synthetic aperture radar (SAR) images. Moreover, the saliency information reflected in the saliency map can highlight the target of interest while suppressing clutter, which is beneficial for better scene understanding. Therefore, in this article, we propose a saliency-guided SSD (S-SSD) for target detection in SAR images, in which we effectively integrate the saliency into the SSD network not only to suggest where to focus on but also to improve the representation capability in complex scenes. The proposed S-SSD contains two separated convolutional backbone subnetwork architectures, one with the original SAR image as input to extract features, and the other with the corresponding saliency map obtained from the modified Itti’s method as input to acquire refined saliency information under supervision. In addition, the dense connection structure, instead of the plain structure used in original SSD, is applied in the two convolutional backbone architectures to utilize multiscale information with fewer parameters. Then, for integrating saliency information to guide the network to emphasize informative regions, multilevel fusion modules are utilized to merge the two streams into a unified framework, thereby making the whole network end-to-end jointly trained. Finally, the convolutional predictors are used to predict targets. The experimental results on the miniSAR real data demonstrate that the proposed S-SSD can achieve better detection performance than state-of-the-art methods. Numéro de notice : A2020-237 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2953936 Date de publication en ligne : 11/12/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2953936 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94983
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3366 - 3376[article]Soil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkCombining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine / Thuan Sarzynski in Remote sensing, vol 12 n° 7 (April 2020)PermalinkWhat, where, and how to transfer in SAR target recognition based on deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkExtracting impervious surfaces from full polarimetric SAR images in different urban areas / Sara Attarchi in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkRadar Vegetation Index for assessing cotton crop condition using RISAT-1 data / Dipanwita Haldar in Geocarto international, vol 35 n° 4 ([15/03/2020])PermalinkDeep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkPrediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)PermalinkRadial interpolation of GPS and leveling data of ground deformation in a resurgent caldera: application to Campi Flegrei (Italy) / Andrea Bevilacqua in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkCombination of linear regression lines to understand the response of Sentinel-1 dual polarization SAR data with crop phenology - case study in Miyazaki, Japan / Emal Wali in Remote sensing, vol 12 n° 1 (January 2020)PermalinkIdentification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)PermalinkInversion de données PolSAR en bande P pour l'estimation de la biomasse forestière / Colette Gelas (2020)PermalinkPermalinkOn the joint exploitation of optical and SAR satellite imagery for grassland monitoring / Anatol Garioud (2020)PermalinkPermalinkPermalinkAn implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkCombining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkContextual filtering methods based on the subbands and subspaces decomposition of complex SAR interferograms / Saoussen Belhadj-Aissa in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 12 n° 12 (December 2019)PermalinkMatching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkOn the value of corner reflectors and surface models in InSAR precise point positioning / Mengshi Yang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkPolarization dependence of azimuth cutoff from quad-pol SAR images / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)PermalinkIntroducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkSoil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations / S. Hamed Alemohammad in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkA temporal phase coherence estimation algorithm and its application on DInSAR pixel selection / Feng Zhao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)PermalinkCombining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (October-2 2019)PermalinkComparative analysis of the accuracy of surface soil moisture estimation from the C- and L-bands / Mohammad El Hajj in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)PermalinkMulti-sensor prediction of Eucalyptus stand volume: A support vector approach / Guilherme Silverio Aquino de Souza in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkRobust multisource remote sensing image registration method based on scene shape similarity / Ming Hao in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)PermalinkSaliency-guided deep neural networks for SAR image change detection / Jie Geng in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)PermalinkAn analytic expression for the phase noise of the goldstein–werner filter / Scott Hensley in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkThe Parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment / Michele Manunta in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)PermalinkIntegration 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)PermalinkComprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])PermalinkObservation et suivi de déformations de surface d'origine anthropique par interférométrie radar satellitaire / Daniel Raucoules in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkPolarimétrie radar complète et partielle pour le suivi des surfaces terrestres / Pierre-Louis Frison in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkPrincipes de l'interférométrie d'images radar pour la mesure de la topographie et des déplacements du sol et avancées récentes / Elisabeth Simonetto in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkThe cause of the 2011 Hawthorne (Nevada) earthquake swarm constrained by seismic and InSAR methods / Xianjie Zha in Journal of geodesy, vol 93 n°6 (June 2019)PermalinkUsing Sentinel-1A DInSAR interferometry and Landsat 8 data for monitoring water level changes in two lakes in Crete, Greece / D.D. Alexakis in Geocarto international, vol 34 n° 7 ([01/06/2019])PermalinkCoastline extraction from SAR images using robust ridge tracing / Dailiang Wang in Marine geodesy, vol 42 n° 3 (May 2019)PermalinkAdvanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure / Maged Marghany (2019)PermalinkPermalinkApport des mesures du radar à synthèse d'ouverture de Sentinel-1 pour l'étude des propriétés du manteau neigeux / Gaëlle Veyssière (2019)PermalinkCartographie des déformations sur le site de colocalisation de Grasse par méthode INSAR / Isabelle Delprat (2019)PermalinkPermalinkDiscriminating ship from radio frequency interference based on noncircularity and non-gaussianity in sentinel-1 SAR imagery / Xiangguang Leng in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkEvaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)PermalinkEvaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)PermalinkGlobal observations of ocean surface winds and waves using spaceborne synthetic aperture radar measurements / Huimin Li (2019)PermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkPermalinkPermalinkMicrowave indices from active and passive sensors for remote sensing applications / Emanuele Santi (2019)PermalinkMultitemporal SAR images denoising and change detection : applications to Sentinel-1 data / Weiying Zhao (2019)PermalinkPermalinkPermalinkPermalinkToward global soil moisture monitoring with sentinel-1 : harnessing assets and overcoming obstacles / Bernhard Bauer-Marschallinger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkUrban deformation monitoring using persistent scatterer Interferometry and SAR tomography / Michele Crosetto (2019)PermalinkVariational learning of mixture wishart model for PolSAR image classification / Qian Wu in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkPolarization orientation angle and polarimetric SAR scattering characteristics of steep terrain / Jong-Sen Lee in IEEE Transactions on geoscience and remote sensing, vol 56 n° 12 (December 2018)PermalinkPotential of Sentinel-1 data for monitoring temperate mixed forest phenology / Pierre-Louis Frison in Remote sensing, vol 10 n° 12 (December 2018)PermalinkInvestigation of the success of monitoring slow motion landslides using Persistent Scatterer Interferometry and GNSS methods / K.O. Hastaoglu in Survey review, vol 50 n° 363 (September 2018)PermalinkA new scheme for urban impervious surface classification from SAR images / Hongsheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)PermalinkA statistical approach to preprocess and enhance C-band SAR images in order to detect automatically marine oil slicks / Zhour Najoui in IEEE Transactions on geoscience and remote sensing, vol 56 n° 5 (May 2018)PermalinkTowards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery / Chunping Qiu in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)PermalinkEstimated location of the seafloor sources of marine natural oil seeps from sea surface outbreaks : A new "source path procedure" applied to the northern Gulf of Mexico / Zhour Najoui in Marine and Petroleum Geology, Vol 91 (March 2018)PermalinkPermalinkCaractérisation et qualification de Modèles Numériques de Surfaces (MNS) - Analyse de la cohérence avec des masques d’eau / Guillaume Sutter (2018)PermalinkCartographie des déformations de surface sur l’île de Taiwan par interférométrie RADAR Sentinel-1 / Miloud Fekaouni (2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)PermalinkEtude préalable à l'installation d'un coin radar sur le site de co-localisation de Calern / Guillaume Schmidt (2018)PermalinkExploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area / Saygin Abdikan in Geocarto international, vol 33 n° 1 (January 2018)PermalinkGeometric multi-wavelet total variation for SAR image time series analysis / Abdourrahmane M. Atto (2018)PermalinkPermalinkPermalinkUtilisation de QGIS en télédétection, ch. 6. Cartographie de la végétation à partir d'images radar Sentinel-1 / Pierre-Louis Frison (2018)PermalinkUtilisation des réseaux de capteurs Géocubes pour la mesure de déformation des volcans en temps réel par GNSS / Mohamed-Amjad Lasri (2018)PermalinkComplex-valued convolutional neural network and its application in polarimetric SAR image classification / Zhimian Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkInSAR data for geohazard assessment in UNESCO World Heritage sites: state-of-the-art and perspectives in the Copernicus era / Deodato Tapete in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)PermalinkInSAR to support sustainable urbanization over compacting aquifers: The case of Toluca Valley, Mexico / Pascal Castellazzi in International journal of applied Earth observation and geoinformation, vol 63 (December 2017)PermalinkMultilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification / Yanqiao Chen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)PermalinkBayesian data combination for the estimation of ionospheric effects in SAR interferograms / Giorgio Gomba in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkIncidence angle dependence of first-year sea ice backscattering coefficient in Sentinel-1 SAR Imagery over the kara sea / Marko P. Mäkynen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkShallow geological structures triggered during the Mw 6.4 Meinong earthquake, southwestern Taiwan / Maryline Le Béon in Terrestrial Atmospheric Oceanic sciences journal, vol 28 n° 5 (October 2017)PermalinkThe potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas / Emanuele Santi in Remote sensing of environment, vol 200 (October 2017)PermalinkA mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform / Bangqian Chen in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)PermalinkRobust object-based multipass InSAR deformation reconstruction / Jian Kang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkCoverage of high biomass forests by the ESA BIOMASS mission under defense restrictions / João M.B. Carreiras in Remote sensing of environment, vol 196 (July 2017)PermalinkIntegration of SSC TerraSAR-X images into multisource rapid mapping / D. Vassilaki in Photogrammetric record, vol 32 n° 158 (June - july 2017)PermalinkAn unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data — A case study in complex temperate forest stands / Sahra Abdullahi in International journal of applied Earth observation and geoinformation, vol 57 (May 2017)PermalinkDeep supervised and contractive neural network for SAR image classification / Jie Geng in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkEstimation of 3-D surface displacement based on InSAR and deformation modeling / Jun Hu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkForest classification and impact of BIOMASS resolution on forest area and aboveground biomass estimation / Michael Schlund in International journal of applied Earth observation and geoinformation, vol 56 (April 2017)PermalinkSurface soil moisture retrieval using the L-band synthetic aperture radar onboard the Soil Moisture Active–Passive Satellite and evaluation at core validation sites / Seung-Bum Kim in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)PermalinkTrace coherence : a new operator for polarimetric and interferometric SAR images / Armando Marino in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)Permalink