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Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination / Frederik Hass in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 12 (December 2020) . - n° 758 Titre : Deep learning for detecting and classifying ocean objects: application of YoloV3 for iceberg–ship discrimination Type de document : Article/Communication Auteurs : Frederik Hass, Auteur ; Jamal Jokar Arsanjani, Auteur Année de publication : 2020 Article en page(s) : n° 758 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] Groenland [Termes IGN] hydrocarbure [Termes IGN] iceberg [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] navire [Termes IGN] océan [Termes IGN] seuillage d'image [Termes IGN] trafic maritime Résumé : (auteur) Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data. Numéro de notice : A2020-808 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9120758 Date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.3390/ijgi9120758 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96953 [article]

A novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen 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 190 - 200 Titre : A novel deep learning instance segmentation model for automated marine oil spill detection Type de document : Article/Communication Auteurs : Shamsudeen Temitope Yekeen, Auteur ; Abdul‐Lateef Balogun, Auteur ; Khamaruzaman B. Wan Yusof, Auteur Année de publication : 2020 Article en page(s) : pp 190 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] hydrocarbure [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] segmentation sémantique [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Résumé : (auteur) The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation. Numéro de notice : A2020-548 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.011 Date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95774 [article]

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Les pérégrinations d'un topographe en Chine / Bernard Flacelière in XYZ, n° 164 (septembre 2020)

Public [article]  inXYZ > n° 164 (septembre 2020) . - pp 61 - 67 Titre : Les pérégrinations d'un topographe en Chine Type de document : Article/Communication Auteurs : Bernard Flacelière, Auteur Année de publication : 2020 Article en page(s) : pp 61 - 67 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Topographie moderne [Termes IGN] géodésie tridimensionnelle [Termes IGN] hydrocarbure [Termes IGN] mission de terrain [Termes IGN] Mongolie intérieure (Chine) [Termes IGN] point géodésique [Termes IGN] système de référence géodésique [Termes IGN] topographe Résumé : (auteur) C'est avec un clin d'oeil à Jules Verne et à son roman d'aventures paru en 1879, les Tribulations d'unChinois en Chine, que le topographe rapporte ici les quelques semaines vécues en Mongolie-Intérieure, à Chengdu au Sichuan et enfin à la capitale Beijing (figure 1). Une compagnie française d'exploration et de production d'hydrocarbures ayant obtenu de la part des autorités chinoises le contrat de développement d'un champ gazier déjà découvert, une campagne de sismique terrestre 3D est programmée, à suivre par des forages d'appréciation puis de développement et enfin la construction d'infrastructures dont des pistes, routes, gazoducs, centres de traitement et d'expédition. Des travaux géodésiques sont donc nécessaires, avec rattachement au système géodésique officiel, établissement d'un réseau de détail et relevés des installations existantes. Comme dans de nombreux pays ayant grandi dans la culture du secret des informations géographiques, le topographe découvrira que la géodésie en Chine n'est pas une sinécure. Numéro de notice : A2020-555 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtSansCL DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95824 [article]

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Applying the environmental sensitivity index for the assessment of the prospective oil spills along the Nile Delta Coast, Egypt / Rasha M. Abou Samra in Geocarto international, vol 35 n° 6 ([01/05/2020])  

Public [article]  inGeocarto international > vol 35 n° 6 [01/05/2020] . - pp 589 - 601 Titre : Applying the environmental sensitivity index for the assessment of the prospective oil spills along the Nile Delta Coast, Egypt Type de document : Article/Communication Auteurs : Rasha M. Abou Samra, Auteur ; Rasha Eissa, Auteur ; Maie El-Gammal, Auteur Année de publication : 2020 Article en page(s) : pp 589 - 601 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes IGN] analyse de sensibilité [Termes IGN] environnement [Termes IGN] hydrocarbure [Termes IGN] impact sur l'environnement [Termes IGN] Nil (delta du) [Termes IGN] Nil (fleuve) [Termes IGN] pétrole [Termes IGN] pollution des mers [Termes IGN] rivage [Termes IGN] Suez, canal de Résumé : (auteur) The Nile Delta is located very close to the Suez Canal, the main route for oil transport in the world, makes it prone to pollution from any accidental oil spills in the Mediterranean Sea. The coast of the Nile Delta is generally arcuate and highly exposed to waves and currents. The present study attempted to perform the environmental sensitivity of the shoreline to oil slicks. Six variables were incorporated together in order to determine the environmental sensitivity index (ESI). Data were collected from different resources and from in situ observations. Results showed that the ESI is generally high for the western section of the Nile Delta, particularly along Alexandria region. Low ESI was observed along the shorelines facing the coastal sand dunes at the middle part of the delta coast. The ESI is an effective approach to delineate the vulnerable coastal areas to marine oil pollution. Numéro de notice : A2020-200 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1533592 Date de publication en ligne : 06/11/2018 En ligne : https://doi.org/10.1080/10106049.2018.1533592 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94870 [article]

A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea / Ying Li in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°12 (20 - 30 March 2020) . - pp 4684 - 4701 Titre : A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea Type de document : Article/Communication Auteurs : Ying Li, Auteur ; Huimin Lu, Auteur ; Zhenduo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4684 - 4701 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] équation polynomiale [Termes IGN] hydrocarbure [Termes IGN] image hyperspectrale [Termes IGN] marée noire [Termes IGN] modèle non linéaire [Termes IGN] pollution des mers [Termes IGN] trigonométrie Résumé : (auteur) Hyperspectral remote sensing is currently being used to detect and monitor marine oil spills that cause damage to the environment. However, nonlinear interactions of oil and water make it difficult to extract their fractional abundances from the spectral response. Improving the modelling of nonlinear hyperspectral mixtures, which is required for a thorough and reliable characterization of the materials in an image, remains a challenging yet fundamental task. This study proposes a new model that combines polynomial and trigonometric systems to understand the nonlinear effects of oil and water spectral response. Although the model is nonlinear, unmixing is performed by solving a linear problem, thus allowing fast computation. Compared to classic polynomial models, the details of nonlinear interactions are better expressed and quantified, and the reconstruction accuracy and endmember abundance estimation are improved for both synthetic and real datasets. Both the polynomial and trigonometric parts of the model play important roles in characterizing nonlinearities, with statistically linear dependence areas covering more than 90% and 30%, respectively, in oil spill images sampled after the Deepwater Horizon explosion. Analysis of the experimental results suggests that the proposed model provides an efficient and accurate unmixing method that can be used to help design oil spill response plans. Numéro de notice : A2020-452 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1723179 Date de publication en ligne : 27/02/2020 En ligne : https://doi.org/10.1080/01431161.2020.1723179 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95540 [article]

A machine learning approach to detect crude oil contamination in a real scenario using hyperspectral remote sensing / Ran Pelta in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

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Hyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])  

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Exploitation of hyperspectral data for assessing vegetation health under exposure to petroleum hydrocarbons / Guillaume Lassalle (2019)  

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Ground displacement measurements / Louis-Marie Gauer (2019)

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Monitoring of marine pollution / Houma Bachari Fouzia (2019)  

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Suivi géodésique des réservoirs souterrains / Eric Henrion (2019)  

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Estimated location of the seafloor sources of marine natural oil seeps from sea surface outbreaks : A new "source path procedure" applied to the northern Gulf of Mexico / Zhour Najoui in Marine and Petroleum Geology, Vol 91 (March 2018)  

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Multi-purposeful application of geospatial data / Rustam B. Rustamov (2018)  

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n° 13 - février 2017 - Chiffres clés de l'énergie, édition 2016 (Bulletin de Datalab) / CGDD Commissariat Général au Développement Durable    

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Wetland monitoring with Global Navigation Satellite System reflectometry / Son V. Nghiem in Earth and space science, vol 4 n° 1 (January 2017)  

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