Centre de documentation
scientifique

Assessment of chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data / Ioannis Moutzouris-Sidiris in Open geosciences, vol 13 n° 1 (January 2021)  

Public [article]  inOpen geosciences > vol 13 n° 1 (January 2021) . - pp 85 - 97 Titre : Assessment of chlorophyll-a concentration from Sentinel-3 satellite images at the Mediterranean Sea using CMEMS open source in situ data Type de document : Article/Communication Auteurs : Ioannis Moutzouris-Sidiris, Auteur ; Konstantinos Topouzelis, Auteur Année de publication : 2021 Article en page(s) : pp 85 - 97 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] chlorophylle [Termes IGN] classification par réseau neuronal [Termes IGN] couleur de l'océan [Termes IGN] image Envisat-MERIS [Termes IGN] image Sentinel-3 [Termes IGN] image Sentinel-OLCI [Termes IGN] Méditerranée, mer Résumé : (auteur) The objective of this study is to evaluate the efficiency of two well-known algorithms (Ocean Colour 4 for MERIS [OC4Me] and neural net [NN]) used in the calculation of chlorophyll-a (Chl-a) from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) compared to in situ measurements covering the Mediterranean Sea. In situ data set, obtained from the Copernicus Marine Environmental Monitoring Service (CMEMS) and more specifically from the data set with the title INSITU_MED_NRT_OBSERVATIONS_013_035, and Chl-a values at different depths were extracted. The concentration of Chl-a at a penetration depth was calculated. Then, water was classified into two categories, Case-1 and Case-2. For Case-2 waters, the OC4Me presents a moderate correlation with the in situ data for a time window of 0–2 h. In contrast with the NN algorithm, where very weak correlations were calculated, lower values of the statistical index of Bias for Case-1 waters were calculated for the OC4Me algorithm. Higher values of Pearson correlation were calculated (r > 0.5) for OC4Me algorithm than NN. OC4Me performed better than NN. Numéro de notice : A2021-487 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1515/geo-2020-0204 Date de publication en ligne : 29/01/2021 En ligne : https://doi.org/10.1515/geo-2020-0204 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97776 [article]

Applications of remote sensing in coastal areas / Konstantinos Topouzelis (2020)  

Public Titre : Applications of remote sensing in coastal areas Type de document : Monographie Auteurs : Konstantinos Topouzelis, Éditeur scientifique ; Apostolos Papakonstantinou, Éditeur scientifique ; Siman Singha, Éditeur scientifique ; et al., Auteur Editeur : Bâle [Suisse] : Multidisciplinary Digital Publishing Institute MDPI Année de publication : 2020 Importance : 288 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-3-03928-659-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse d'image orientée objet [Termes IGN] classification orientée objet [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification pixellaire [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] érosion côtière [Termes IGN] falaise [Termes IGN] habitat (nature) [Termes IGN] herbier marin [Termes IGN] image PlanetScope [Termes IGN] modèle numérique de surface [Termes IGN] surveillance du littoral Résumé : (éditeur) Coastal areas are remarkable regions with high spatiotemporal variability. A large population is affected by their physical and biological processes—resulting from effects on tourism to biodiversity and productivity. Coastal ecosystems perform several critical ecosystem services and functions, such as water oxygenation and nutrients provision, seafloor and beach stabilization (as sediment is controlled and trapped within the rhizomes of the seagrass meadows), carbon burial, as areas for nursery, and as refuge for several commercial and endemic species. Knowledge of the spatial distribution of marine habitats is prerequisite information for the conservation and sustainable use of marine resources. Remote sensing from UAVs to spaceborne sensors is offering a unique opportunity to measure, analyze, quantify, map, and explore the processes on the coastal areas at high temporal frequencies. This Special Issue on “Application of Remote Sensing in Coastal Areas” is specifically addresses those successful applications—from local to regional scale—in coastal environments related to ecosystem productivity, biodiversity, sea level rise. Note de contenu : 1- Monitoring cliff erosion with LiDAR surveys and Bayesian network-based data analysis 2- Cubesats allow high spatiotemporal estimates of satellite-derived bathymetry 3- Comparison of Pixel- and object-based classification methods of unmanned aerial vehicle data applied to coastal dune vegetation communities: Casal Borsetti case stud 4- Capturing coastal dune natural vegetation types using a phenology-based mapping approach: The potential of Sentinel-2 5- Sub-pixel waterline extraction: Characterising accuracy and sensitivity to indices and spectra 6- Satellite observations of wind wake and associated oceanic thermal responses: A case study of Hainan Island wind wake 7- Comparison of true-color and multispectral unmanned aerial systems imagery for marine habitat mapping using object-based image analysis 8- Spatial and temporal variability of open-ocean barrier islands along the Indus Delta region 9- Characterizing and monitoring ground settlement of marine reclamation land of Xiamen New Airport, China with Sentinel-1 SAR datasets 10- Deriving high spatial-resolution coastal topography from sub-meter satellite stereo imagery 11- Photon-counting Lidar: An adaptive signal detection method for different land cover types in coastal area 12- Automatic semi-global artificial shoreline subpixel localization algorithm for Landsat imagery 13- Analysis of ship detection performance with full-, compact- and dual-polarimetric SAR 14- Sea ice extent detection in the Bohai Sea using Sentinel-3 OLCI data Numéro de notice : 28689 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.3390/books978-3-03928-659-1 En ligne : https://doi.org/10.3390/books978-3-03928-659-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100128

Development of a network-based method for unmixing of hyperspectral data / V. Karathanassi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 3 (March 2012) . - pp 839 - 849 Titre : Development of a network-based method for unmixing of hyperspectral data Type de document : Article/Communication Auteurs : V. Karathanassi, Auteur ; D. Sykas, Auteur ; Konstantinos Topouzelis, Auteur Année de publication : 2012 Article en page(s) : pp 839 - 849 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] distance euclidienne [Termes IGN] image hyperspectrale Résumé : (Auteur) This paper presents a new nonlinear unmixing method. Based on relative distances which imply nonlinearity, the method introduces the “fractional distance” as a key variable that quantifies interactions between pixels and endmembers. Relationships between fractional distances and abundance fractions are built through networks. Because an equal spectral mixture of ground spectral classes present on the surface sensed is likely impossible, the proposed method, due to its mathematical concept, reveals unknown endmembers. Three versions of the method have been developed: the nonconstrained, the sum-to-one, and the fully constrained versions. Evaluation of the method using synthetic and real data showed that the method is robust with clear and interpretable results and provides reliable abundance fractions, particularly the sum-to-one and the fully constrained versions of the method. The new unmixing method has also been compared with the fully constrained least squares method. Numéro de notice : A2012-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2163412 Date de publication en ligne : 15/09/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2163412 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31547 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes / Konstantinos Topouzelis in Geocarto international, vol 24 n° 3 (June - July 2009)  

Public [article]  inGeocarto international > vol 24 n° 3 (June - July 2009) . - pp 179 - 191 Titre : Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes Type de document : Article/Communication Auteurs : Konstantinos Topouzelis, Auteur ; V. Karathanassi, Auteur ; P. Pavlaskis, Auteur ; D. Rokos, Auteur Année de publication : 2009 Article en page(s) : pp 179 - 191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification par réseau neuronal [Termes IGN] détection [Termes IGN] fonction de base radiale [Termes IGN] hydrocarbure [Termes IGN] image radar [Termes IGN] marée noire [Termes IGN] Perceptron multicouche [Termes IGN] pollution des mers [Termes IGN] rétrodiffusion Résumé : (Auteur) Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection. Numéro de notice : A2009-186 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802488526 Date de publication en ligne : 19/05/2009 En ligne : https://doi.org/10.1080/10106040802488526 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29816 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-09031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Detection and discrimination between oil spills and look-alike phenomena through neural networks / Konstantinos Topouzelis in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 4 (September 2007)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 62 n° 4 (September 2007) . - pp 264 - 270 Titre : Detection and discrimination between oil spills and look-alike phenomena through neural networks Type de document : Article/Communication Auteurs : Konstantinos Topouzelis, Auteur ; V. Karathanassi, Auteur ; P. Pavlakis, Auteur ; D. Rokos, Auteur Année de publication : 2007 Article en page(s) : pp 264 - 270 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] analyse discriminante [Termes IGN] classification par réseau neuronal [Termes IGN] détection automatique [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] radargrammétrie Résumé : (Auteur) Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in the marine environment, as their recording is independent of clouds and weather. Dark formations can be caused by man made actions (e.g. oil spill discharging) or natural ocean phenomena (e.g. natural slicks, wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they damp the capillary and short gravity sea waves. The ability of neural networks to detect dark formations in high resolution SAR images and to discriminate oil spills from look-alike phenomena simultaneously was examined. Two different neural networks are used; one to detect dark formations and the second one to perform a classification to oil spills or look-alikes. The proposed method is very promising in detecting dark formations and discriminating oil spills from look-alikes as it detects with an overall accuracy of 94% the dark formations and discriminate correctly 89% of examined cases. Numéro de notice : A2007-428 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2007.05.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2007.05.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28791 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-07061	SL	Revue	Centre de documentation	Revues en salle	Disponible