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What, 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)
[article]
Titre : What, where, and how to transfer in SAR target recognition based on deep CNNs Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 2324 - 2336 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de cible
[Termes IGN] données multisources
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] source de données
[Termes IGN] transmission de donnéesRésumé : (auteur) Deep convolutional neural networks (DCNNs) have attracted much attention in remote sensing recently. Compared with the large-scale annotated data set in natural images, the lack of labeled data in remote sensing becomes an obstacle to train a deep network very well, especially in synthetic aperture radar (SAR) image interpretation. Transfer learning provides an effective way to solve this problem by borrowing knowledge from the source task to the target task. In optical remote sensing application, a prevalent mechanism is to fine-tune on an existing model pretrained with a large-scale natural image data set, such as ImageNet. However, this scheme does not achieve satisfactory performance for SAR applications because of the prominent discrepancy between SAR and optical images. In this article, we attempt to discuss three issues that are seldom studied before in detail: 1) what network and source tasks are better to transfer to SAR targets; 2) in which layer are transferred features more generic to SAR targets; and 3) how to transfer effectively to SAR targets recognition. Based on the analysis, a transitive transfer method via multisource data with domain adaptation is proposed in this article to decrease the discrepancy between the source data and SAR targets. Several experiments are conducted on OpenSARShip. The results indicate that the universal conclusions about transfer learning in natural images cannot be completely applied to SAR targets, and the analysis of what and where to transfer in SAR target recognition is helpful to decide how to transfer more effectively. Numéro de notice : A2020-195 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947634 Date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947634 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94863
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2324 - 2336[article]Complex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? / Arthur Delorme in Tectonics, vol 39 n° 2 (February 2020)
[article]
Titre : Complex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? Type de document : Article/Communication Auteurs : Arthur Delorme, Auteur ; Raphaël Grandin, Auteur ; Yann Klinger, Auteur ; Marc Pierrot-Deseilligny , Auteur ; Nathalie Feuillet, Auteur ; Eric Jacques, Auteur ; Ewelina Rupnik , Auteur ; Yu Morishita, Auteur Année de publication : 2020 Projets : Université de Paris / Clerici, Christine Article en page(s) : n° e2019TC005596 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] compensation locale par faisceaux
[Termes IGN] déformation de la croute terrestre
[Termes IGN] données GPS
[Termes IGN] données spatiotemporelles
[Termes IGN] effondrement de terrain
[Termes IGN] géodésie physique
[Termes IGN] image à résolution submétrique
[Termes IGN] image ALOS
[Termes IGN] image Pléiades-HR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Italie
[Termes IGN] MicMac
[Termes IGN] modèle par fonctions rationnelles
[Termes IGN] séismeRésumé : (Auteur) The relation between slip at the near surface and at depth during earthquakes is still not fully resolved at the moment. This deficiency leads to large uncertainties in the evaluation of the magnitude of past earthquakes based on surfaceobservations, which is the only accessible evidence for such events. A better knowledge of the way slip distributes over distinct rupture strands within the first few kilometers from the surface would contribute greatly to reduce these uncertainties. The 30 October 2016 Mw6.5 Norcia earthquake has been captured by a variety of geodetic techniques, which provide access to the slip distribution both at depth and at the ground surface, with an unprecedented level of detail for a normal-faulting earthquake. Wefirst present coseismic surface offset measurements from correlation of optical satellite imagesof sub-metric resolution, which are compared to field observations made shortly after the earthquake. Based on a joint inversion of optical data together withInSAR and GPS data, we then propose a rupture model that explains the observations both at far-field and near-field scales. Finally we explore different rupture geometriesat shallow depth, in an attempt to better explain the near-field deformation (i.e. within the first hundreds of meters around the fault)observed at the surface. Despite the fact that the solution is not unique, several lines of evidence suggest that gravity processes could be locally involved, which interfere with the dominant tectonic processes. Numéro de notice : A2020-039 Affiliation des auteurs : LASTIG+Ext (2016-2019) Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1029/2019TC005596 Date de publication en ligne : 03/01/2020 En ligne : https://dx.doi.org/10.1029/2019TC005596 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94501
in Tectonics > vol 39 n° 2 (February 2020) . - n° e2019TC005596[article]Documents numériques
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Complex deformation at shallow depth... - pdf auteurAdobe Acrobat PDF A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams / Benjamin Swan in Transactions in GIS, Vol 24 n° 1 (February 2020)
[article]
Titre : A LiDAR–optical data fusion approach for identifying and measuring small stream impoundments and dams Type de document : Article/Communication Auteurs : Benjamin Swan, Auteur ; Robert Griffin, Auteur Année de publication : 2020 Article en page(s) : pp 174 - 188 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] Alabama (Etats-Unis)
[Termes IGN] barrage
[Termes IGN] cours d'eau
[Termes IGN] données lidar
[Termes IGN] écosystème
[Termes IGN] fusion de données
[Termes IGN] image à haute résolution
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] modèle numérique de surface
[Termes IGN] Normalized Difference Water Index
[Termes IGN] ressources en eau
[Termes IGN] semis de points
[Termes IGN] surveillance hydrologique
[Termes IGN] système d'information géographiqueRésumé : (auteur) This article outlines a semi‐autonomous approach for using a fusion of light detection and ranging (LiDAR) and optical remote sensing data to identify and measure small impoundments (SIs) and their dams. Quantifying such water bodies as hydrologic network features is critical for ecosystem and species conservation, emergency management, and water resource planning; however, such features are incompletely mapped at national and state levels. By merging an airborne LiDAR‐derived point cloud with a normalized water index using airborne optical imagery we demonstrate an improvement upon single‐source methods for identifying these water bodies; classification accuracies increased over 10% by using this multi‐source fusion method. Furthermore, the method presented here illustrates a cost‐effective pathway to improve the National Inventory of Dams (NID) and includes a framework for estimating dam heights, with results showing strong correlations between derived dam heights and those recorded in the NID (r=.78). With the steady increase in available LiDAR coverage, the 87,000+ dams in the NID could be updated using this technique, a method which could also be expanded for global inventories of SIs and dams. Numéro de notice : A2020-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12595 Date de publication en ligne : 13/11/2019 En ligne : https://doi.org/10.1111/tgis.12595 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94694
in Transactions in GIS > Vol 24 n° 1 (February 2020) . - pp 174 - 188[article]Prediction 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)
[article]
Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Projets : SEBIOREF / Ouin, Annie Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] biodiversité végétale
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] Haute-Garonne (31)
[Termes IGN] image radar moirée
[Termes IGN] image RapidEye
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] indice de diversité
[Termes IGN] indice de végétation
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] prairie
[Termes IGN] série temporelle
[Termes IGN] taxinomieRésumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Affiliation des auteurs : UPEM-LASTIG+Ext (2016-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 Date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296
in Remote sensing of environment > Vol 237 (February 2020) . - 13 p.[article]Combining 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)
[article]
Titre : Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture Type de document : Article/Communication Auteurs : Dino Lenco, Auteur ; Roberto Interdonato, Auteur ; Raffaele Gaetano, Auteur ; Ho Tong Minh Dinh, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] apprentissage profond
[Termes IGN] Burkina Faso
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion d'images
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Sentinel-SAR
[Termes IGN] occupation du sol
[Termes IGN] Réunion, île de la
[Termes IGN] série temporelle
[Termes IGN] utilisation du solRésumé : (auteur) The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal. Numéro de notice : A2019-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.016 Date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94186
in ISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019)[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019121 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019123 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019122 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Matching 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)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)PermalinkExploring the synergy between Landsat and ASAR towards improving thematic mapping accuracy of optical EO data / Alexander Cass in Applied geomatics, vol 11 n° 3 (September 2019)PermalinkChallenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (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)PermalinkExploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)PermalinkJoint analysis of SAR and optical satellite images time series for grassland event detection / Anatol Garioud (2019)PermalinkPermalinkPermalinkMonitoring crops water needs at high spatio-temporal resolution by synergy of optical / thermal and radar observations / Abdelhakim Amazirh (2019)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)PermalinkNouvelle méthode en cascade pour la classification hiérarchique multi-temporelle ou multi-capteur d'images satellitaires haute résolution / Ihsen Hedhli in Revue Française de Photogrammétrie et de Télédétection, n° 216 (février 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)PermalinkSentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping / Jan Haas in Remote Sensing Applications: Society and Environment, RSASE, vol 8 (November 2017)PermalinkIntersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)PermalinkFusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area / Mohamed Barakat A. Gibril in Geocarto international, vol 32 n° 7 (July 2017)PermalinkA novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkHyperspectral SAR / Matthew Ferrara in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)Permalink