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X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 12 - 23 Titre : X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data Type de document : Article/Communication Auteurs : Danfeng Hong, Auteur ; Naoto Yokoya, Auteur ; Gui-Song Sia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 12 - 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] bruit blanc [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] scène urbaine [Termes descripteurs IGN] transmission de données Résumé : (auteur) This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, by learning to transfer more discriminative information from a small-scale hyperspectral image (HSI) into the classification task using a large-scale MSI or SAR data. Significantly, X-ModalNet generalizes well, owing to propagating labels on an updatable graph constructed by high-level features on the top of the network, yielding semi-supervised cross-modality learning. We evaluate X-ModalNet on two multi-modal remote sensing datasets (HSI-MSI and HSI-SAR) and achieve a significant improvement in comparison with several state-of-the-art methods. Numéro de notice : A2020-544 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.06.014 date de publication en ligne : 11/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.06.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95770 [article]

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Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series / Sébastien Giordano in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 7 (July 2020) . - pp 431 - 441 Titre : Improved crop classification with rotation knowledge using Sentinel-1 and -2 time series Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Simon Bailly, Auteur ; Loïc Landrieu , Auteur ; Nesrine Chehata , Auteur Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Article en page(s) : pp 431 - 441 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] Alpes-de-haute-provence (04) [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] parcelle agricole [Termes descripteurs IGN] photo-identification [Termes descripteurs IGN] Seine-et-Marne (77) [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surface cultivée Résumé : (Auteur) Leveraging the recent availability of accurate, frequent, and multimodal (radar and optical) Sentinel-1 and -2 acquisitions, this paper investigates the automation of land parcel identification system (LPIS) crop type classification. Our approach allows for the automatic integration of temporal knowledge, i.e., crop rotations using existing parcel-based land cover databases and multi-modal Sentinel-1 and -2 time series. The temporal evolution of crop types was modeled with a linear-chain conditional random field, trained with time series of multimodal (radar and optical) satellite acquisitions and associated LPIS. Our model was tested on two study areas in France (≥ 1250 km2) which show different crop types, various parcel sizes, and agricultural practices: . the Seine et Marne and the Alpes de Haute-Provence classified accordingly to a fine national 25-class nomenclature. We first trained a Random Forest classifier without temporal structure to achieve 89.0% overall accuracy in Seine et Marne (10 classes) and 73% in Alpes de Haute-Provence (14 classes). We then demonstrated experimentally that taking into account the temporal structure of crop rotation with our model resulted in an increase of 3% to +5% in accuracy. This increase was especially important (+12%) for classes which were poorly classified without using the temporal structure. A stark positive impact was also demonstrated on permanent crops, while it was fairly limited or even detrimental for annual crops. Numéro de notice : A2020-382 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.7.431 date de publication en ligne : 01/07/2020 En ligne : https://doi.org/10.14358/PERS.86.7.431 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95428 [article]

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Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France / Edward Salameh in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 98 - 120 Titre : Intertidal topography mapping using the waterline method from Sentinel-1 & -2 images: The examples of Arcachon and Veys Bays in France Type de document : Article/Communication Auteurs : Edward Salameh, Auteur ; Frédéric Frappart, Auteur ; Imen Turki, Auteur ; Benoit Laignel, Auteur Année de publication : 2020 Article en page(s) : pp 98 - 120 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] aménagement du littoral [Termes descripteurs IGN] Arcachon (bassin d') [Termes descripteurs IGN] carte topographique [Termes descripteurs IGN] Cotentin [Termes descripteurs IGN] estran [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] hydrodynamique [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] niveau de l'eau [Termes descripteurs IGN] sédiment [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] trait de côte [Termes descripteurs IGN] zone tampon Résumé : (auteur) Intertidal flats lying as a buffer zone between land and sea provide critical services including protection against storm surges and coastal flooding. These environments are characterized by a continuous redistribution of sediment and changes in topography. Sea level rise, anthropogenic pressures, and their related stressors have a considerable impact on these areas and are expected to put them under more stress; hence the increased need for frequent and updated topography maps. Comparing to traditional surveying approaches, spaceborne remote sensing is able to provide topography maps more frequently with a lower cost and a higher coverage. The latter is currently considered as an established tool for measuring intertidal topography. In this study, an improved approach of the waterline method was developed to derive intertidal Digital Elevation Models (DEMs). The changes include a faster, more efficient and quasi-automatic detection and post-processing of waterlines. The edge detection technique consists in combining a k-means based segmentation and an active contouring procedure. This method was designed to generate closed contours in order to enable an automatization of the post-processing of the extracted waterlines. The waterlines were extracted from Sentinel-1 and Sentinel-2 images for two bays located on the French Coast: the Arcachon lagoon and the Bay of Veys. DEMs were generated for the Arcachon Bay between 2015 and 2018, and for the Bay of Veys between 2016 and 2018 using satellite acquisitions made during summer (low storm activity period). The comparison of the generated DEMs with lidar observations showed an error of about 19–25 cm. This study also demonstrated that the waterline method applied to Sentinel images is suitable for monitoring the morpho-sedimentary evolution in intertidal areas. By comparing the DEMs generated between 2016 and 2018, the Arcachon Bay and the Bay of Veys experienced net volume losses of 1.12 × 106 m3 and 0.70 × 106 m3 respectively. The generated DEMs provide useful and needed information for several scientific applications (e.g., sediment balance, hydrodynamic modelling), but also for authorities and stakeholders for coastal management and implementation of ecosystem protection policies. Numéro de notice : A2020-138 Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.003 date de publication en ligne : 13/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94756 [article]

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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2324 - 2336 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 descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] données multisources [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] source de données [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] transmission de données Résumé : (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 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 [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)    

Public [article]  inTectonics > vol 39 n° 2 (February 2020) . - n° e2019TC005596 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 descripteurs IGN] analyse comparative [Termes descripteurs IGN] compensation locale par faisceaux [Termes descripteurs IGN] corrélation d'images [Termes descripteurs IGN] déformation de la croute terrestre [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] géodésie physique [Termes descripteurs IGN] image à résolution submétrique [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] Italie [Termes descripteurs IGN] MicMac [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs IGN] séisme Résumé : (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 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 [article]

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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)  

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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)  

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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)  

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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)  

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Multi-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)  

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Robust 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)  

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Exploring 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)  

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Challenges in grassland mowing event detection with multimodal Sentinel images / Anatol Garioud (2019)  

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Evaluating 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)  

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Evaluation of time-series SAR and optical images for the study of winter land-use / Julien Denize (2019)  

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