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Marrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images / Guillemette Fonteix in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
[article]
Titre : Marrying deep learning and data fusion for accurate semantic labeling of Sentinel-2 images Type de document : Article/Communication Auteurs : Guillemette Fonteix, Auteur ; M. Swaine, Auteur ; M. Leras, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 101 - 107 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] carte de confiance
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] fusion d'images
[Termes IGN] image optique
[Termes IGN] image Sentinel-MSI
[Termes IGN] segmentation sémantique
[Termes IGN] série temporelleRésumé : (auteur) The understanding of the Earth through global land monitoring from satellite images paves the way towards many applications including flight simulations, urban management and telecommunications. The twin satellites from the Sentinel-2 mission developed by the European Space Agency (ESA) provide 13 spectral bands with a high observation frequency worldwide. In this paper, we present a novel multi-temporal approach for land-cover classification of Sentinel-2 images whereby a time-series of images is classified using fully convolutional network U-Net models and then coupled by a developed probabilistic algorithm. The proposed pipeline further includes an automatic quality control and correction step whereby an external source can be introduced in order to validate and correct the deep learning classification. The final step consists of adjusting the combined predictions to the cloud-free mosaic built from Sentinel-2 L2A images in order for the classification to more closely match the reference mosaic image. Numéro de notice : A2021-492 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-3-2021-101-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-3-2021-101-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97957
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 101 - 107[article]Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)
[article]
Titre : Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data Type de document : Article/Communication Auteurs : Claire Teillet, Auteur ; Benjamin Pillot, Auteur ; Thibault Catry, Auteur ; Laurent Demagistri, Auteur ; Dominique Lyszczarz, Auteur ; Marc Lang, Auteur ; Pierre Couteron, Auteur ; Nicolas Barbier, Auteur ; Arsène Adou Kouassi, Auteur ; Quentin Gunther , Auteur ; Nadine Dessay, Auteur Année de publication : 2021 Projets : GeoSud / , TOSCA / Article en page(s) : n° 2398 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Brasilia
[Termes IGN] caractérisation
[Termes IGN] Côte d'Ivoire
[Termes IGN] empreinte
[Termes IGN] image Pléiades-HR
[Termes IGN] image Sentinel-MSI
[Termes IGN] paysage urbain
[Termes IGN] texture d'image
[Termes IGN] zone urbaineRésumé : (auteur) Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited. Numéro de notice : A2021-505 Affiliation des auteurs : ENSG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13122398 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.3390/rs13122398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98125
in Remote sensing > vol 13 n° 12 (June-2 2021) . - n° 2398[article]Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 / Dimitris Poursanidis in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
[article]
Titre : Cloud-native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel-2 Type de document : Article/Communication Auteurs : Dimitris Poursanidis, Auteur ; Dimosthenis Traganos, Auteur ; Luisa Teixeira, Auteur ; Aurélie Shapiro, Auteur ; Lara Muaves, Auteur Année de publication : 2021 Article en page(s) : pp 275 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification et arbre de régression
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] écosystème
[Termes IGN] Google Earth Engine
[Termes IGN] habitat (nature)
[Termes IGN] image Sentinel-MSI
[Termes IGN] Mozambique
[Termes IGN] récif corallien
[Termes IGN] réserve naturelle
[Termes IGN] surveillance écologiqueRésumé : (auteur) The lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi-automated workflow in the cloud that uses freely available Sentinel-2 data from the European Space Agency (ESA) Copernicus programme to derive information on near-shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end-to-end cloud-based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud-free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite-derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub-pixel–level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow-water ecosystems which are of great significance to local communities and building resilience towards climate change. Numéro de notice : A2021-733 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.187 Date de publication en ligne : 29/11/2020 En ligne : https://doi.org/10.1002/rse2.187 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98679
in Remote sensing in ecology and conservation > vol 7 n° 2 (June 2021) . - pp 275 - 291[article]Discovery of new colonies by Sentinel2 reveals good and bad news for emperor penguins / Peter T. Fretwell in Remote sensing in ecology and conservation, vol 7 n° 2 (June 2021)
[article]
Titre : Discovery of new colonies by Sentinel2 reveals good and bad news for emperor penguins Type de document : Article/Communication Auteurs : Peter T. Fretwell, Auteur ; Philip N. Trathan, Auteur Année de publication : 2021 Article en page(s) : pp 139 - 153 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Antarctique
[Termes IGN] Aves
[Termes IGN] image Sentinel-MSIMots-clés libres : manchot empereur Aptenodytes forsteri Résumé : (auteur) The distribution of emperor penguins is circumpolar, with 54 colony locations currently reported of which 50 are currently extant as of 2019. Here we report on eight newly discovered colonies and confirm the rediscovery of three breeding sites, only previously reported in the era before Very High Resolution satellite imagery was available, making a total of 61 breeding locations. This represents an increase of ~20% in the number of breeding sites, but, as most of the colonies appear to be small, they may only increase the total population by around 5–10%. The discoveries have been facilitated by the use of Sentinel2 satellite imagery, which has a higher resolution and more efficient search mechanism than the Landsat data previously used to search for colonies. The small size of these new colonies indicates that considerations of reproductive output in relation to metabolic rate during huddling is likely to be of interest. Some of the colonies exist in offshore habitats, something not previously reported for emperor penguins. Comparison with recent modelling results show that the geographic locations of all the newly found colonies are in areas likely to be highly vulnerable under business-as-usual greenhouse gas emissions scenarios, suggesting that population decreases for the species will be greater than previously thought. Numéro de notice : A2021-732 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1002/rse2.176 Date de publication en ligne : 04/08/2020 En ligne : https://doi.org/10.1002/rse2.176 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98678
in Remote sensing in ecology and conservation > vol 7 n° 2 (June 2021) . - pp 139 - 153[article]Multiscale cloud detection in remote sensing images using a dual convolutional neural network / Markku Luotamo in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
[article]
Titre : Multiscale cloud detection in remote sensing images using a dual convolutional neural network Type de document : Article/Communication Auteurs : Markku Luotamo, Auteur ; Sari Metsämäki, Auteur ; Arto Klami, Auteur Année de publication : 2021 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification pixellaire
[Termes IGN] détection des nuages
[Termes IGN] granularité d'image
[Termes IGN] image Sentinel-MSI
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence, features that have a large spatial extent still cause challenges in tasks, such as cloud masking. To support a wider scale of spatial features while simultaneously reducing computational requirements for large satellite images, we propose an architecture of two cascaded CNN model components successively processing undersampled and full-resolution images. The first component distinguishes between patches in the inner cloud area from patches at the cloud’s boundary region. For the cloud-ambiguous edge patches requiring further segmentation, the framework then delegates computation to a fine-grained model component. We apply the architecture to a cloud detection data set of complete Sentinel-2 multispectral images, approximately annotated for minimal false negatives in a land-use application. On this specific task and data, we achieve a 16% relative improvement in pixel accuracy over a CNN baseline based on patching. Numéro de notice : A2021-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3015272 Date de publication en ligne : 21/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3015272 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97781
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp[article]Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkA compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)PermalinkAboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)PermalinkAssessing forest phenology: A multi-scale comparison of near-surface (UAV, spectral reflectance sensor, PhenoCam) and satellite (MODIS, Sentinel-2) remote sensing / Shangharsha Thapa in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkDecision-level and feature-level integration of remote sensing and geospatial big data for urban land use mapping / Jiadi Yin in Remote sensing, vol 13 n° 8 (April-2 2021)PermalinkLeaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([15/04/2021])PermalinkPotentialité des données satellitaires Sentinel-2 pour la cartographie de l’impact des feux de végétation en Afrique tropicale : application au Togo / Yawo Konko in Bois et forêts des tropiques, n° 347 ([02/04/2021])PermalinkAtmospheric correction of Sentinel-3/OLCI data for mapping of suspended particulate matter and chlorophyll-a concentration in Belgian turbid coastal waters / Quinten Vanhellemont in Remote sensing of environment, Vol 256 (April 2020)PermalinkExtraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)PermalinkShoreline changes along Northern Ibaraki Coast after the great East Japan earthquake of 2011 / Quang Nguyen Hao in Remote sensing, vol 13 n° 7 (April-1 2021)Permalink