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Multi-objective CNN-based algorithm for SAR despeckling / Sergio Vitale in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)
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Titre : Multi-objective CNN-based algorithm for SAR despeckling Type de document : Article/Communication Auteurs : Sergio Vitale, Auteur ; Giampaolo Ferraioli, Auteur ; Vito Pascazio, Auteur Année de publication : 2021 Article en page(s) : pp 9336 - 9349 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] chatoiement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] filtre de déchatoiement
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] restauration d'imageRésumé : (auteur) Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous. Numéro de notice : A2021-810 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3034852 Date de publication en ligne : 16/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3034852 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98874
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 11 (November 2021) . - pp 9336 - 9349[article]Tidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region / Paulo Victor N. Araújo in Natural Hazards and Earth System Sciences, vol 21 n° 11 (November 2021)
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Titre : Tidal flood area mapping in the face of climate change scenarios: case study in a tropical estuary in the Brazilian semi-arid region Type de document : Article/Communication Auteurs : Paulo Victor N. Araújo, Auteur ; Venerando E. Amaro, Auteur ; Leonlene S. Aguiar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3353 - 3366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Brésil
[Termes IGN] carte thématique
[Termes IGN] cartographie des risques
[Termes IGN] changement climatique
[Termes IGN] données lidar
[Termes IGN] estuaire
[Termes IGN] image Landsat-8
[Termes IGN] image Landsat-OLI
[Termes IGN] image Radarsat
[Termes IGN] marée océanique
[Termes IGN] modèle numérique de surface
[Termes IGN] montée du niveau de la mer
[Termes IGN] risque naturel
[Termes IGN] submersion marine
[Termes IGN] zone inondable
[Termes IGN] zone semi-arideRésumé : (auteur) Previous studies on tidal flood mapping are mostly through continental- and/or global-scale approaches. Moreover, the few works on local-scale perception are concentrated in Europe, Asia, and North America. Here, we present a case study approaching a tidal flood risk mapping application in the face of climate change scenarios in a region with a strong environmental and social appeal. The study site is an estuarine cut in the Brazilian semi-arid region, covering part of two state conservation units, which has been suffering severe consequences from tidal flooding in recent years. In this case study, we used high-geodetic-precision data (lidar DEM), together with robust tidal return period statistics and data from current sea level rise scenarios. We found that approximately 327.60 km2 of the estuary is under tidal flood risk and in need of mitigation measures. This case study can serve as a basis for future management actions, as well as a model for applying risk mapping in other coastal areas. Numéro de notice : A2021-127 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.5194/nhess-21-3353-2021 Date de publication en ligne : 09/11/2021 En ligne : https://doi.org/10.5194/nhess-21-3353-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99321
in Natural Hazards and Earth System Sciences > vol 21 n° 11 (November 2021) . - pp 3353 - 3366[article]PolSAR ship detection based on neighborhood polarimetric covariance matrix / Tao Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
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Titre : PolSAR ship detection based on neighborhood polarimetric covariance matrix Type de document : Article/Communication Auteurs : Tao Liu, Auteur ; Ziyuan Yang, Auteur ; Armando Marino, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4874 - 4887 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] détection d'objet
[Termes IGN] données polarimétriques
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] matrice de covariance
[Termes IGN] navire
[Termes IGN] polarimétrie radar
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) The detection of small ships in polarimetric synthetic aperture radar (PolSAR) images is still a topic for further investigation. Recently, patch detection techniques, such as superpixel-level detection, have stimulated wide interest because they can use the information contained in similarities among neighboring pixels. In this article, we propose a novel neighborhood polarimetric covariance matrix (NPCM) to detect the small ships in PolSAR images, leading to a significant improvement in the separability between ship targets and sea clutter. The NPCM utilizes the spatial correlation between neighborhood pixels and maps the representation for a given pixel into a high-dimensional covariance matrix by embedding spatial and polarization information. Using the NPCM formalism, we apply a standard whitening filter, similar to the polarimetric whitening filter (PWF). We show how the inclusion of neighborhood information improves the performance compared with the traditional polarimetric covariance matrix. However, this is at the expense of a higher computation cost. The theory is validated via the simulated and measured data under different sea states and using different radar platforms. Numéro de notice : A2021-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022181 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3018638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97780
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4874 - 4887[article]Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)
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Titre : Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images Type de document : Article/Communication Auteurs : Bin Zhang, Auteur ; Ling Chang, Auteur ; Alfred Stein, Auteur Année de publication : 2021 Article en page(s) : pp 222 - 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] déformation de surface
[Termes IGN] données spatiotemporelles
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] points homologues
[Termes IGN] série temporelleRésumé : (auteur) A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 . We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets. Numéro de notice : A2021-414 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.005 Date de publication en ligne : 08/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97745
in ISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 222 - 236[article]
Titre : Polarimetric Synthetic Aperture Radar : principles and application Type de document : Monographie Auteurs : Irena Hajnsek, Auteur ; Yves-Louis Desnos, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2021 Collection : Remote sensing and digital image processing num. 25 Importance : 294 p. ISBN/ISSN/EAN : 978-3-030-56504-6 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] agriculture
[Termes IGN] bande X
[Termes IGN] cryosphère
[Termes IGN] détection d'arbres
[Termes IGN] détection du bâti
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Radarsat
[Termes IGN] image Terra
[Termes IGN] occupation du sol
[Termes IGN] polarimétrie radar
[Termes IGN] radar à antenne synthétique
[Termes IGN] zone humideIndex. décimale : 35.22 Télédétection en hyperfréquence - Traitement d'image radar Résumé : (Editeur) This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans. Note de contenu : - Basic Principles of SAR Polarimetry
- Forest Applications
- Agriculture and Wetland Applications
- Cryosphere Applications
- Urban Applications
- Ocean ApplicationsNuméro de notice : 26542 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/URBANISME Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-56504-6 En ligne : http://doi.org/10.1007/978-3-030-56504-6 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97766 Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
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)
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)
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])
PermalinkCoastline extraction from SAR images using robust ridge tracing / Dailiang Wang in Marine geodesy, vol 42 n° 3 (May 2019)
PermalinkPermalinkEvaluation 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)
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