Centre dedocumentation
scientifique

Automatic detection of thin oil films on water surfaces in ultraviolet imagery / Ming Xie in Photogrammetric record, vol 38 n° 181 (March 2023)  

Public [article]  inPhotogrammetric record > vol 38 n° 181 (March 2023) . - pp 47 - 62 Titre : Automatic detection of thin oil films on water surfaces in ultraviolet imagery Type de document : Article/Communication Auteurs : Ming Xie, Auteur ; Xiurui Zhang, Auteur ; Ying Li, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 47 - 62 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] détection automatique [Termes IGN] filtre optique [Termes IGN] hydrocarbure [Termes IGN] image AVIRIS [Termes IGN] marée noire [Termes IGN] niveau de gris (image) [Termes IGN] rayonnement ultraviolet [Termes IGN] segmentation d'image [Termes IGN] seuillage binaire [Termes IGN] surface de la mer Résumé : (auteur) Among the various remote sensing technologies that have been applied to monitor oil spills on the sea surface, passive ultraviolet (UV) imaging is a controversial one that has raised some disputes in the community of oil spill remote sensing. As a result, the research and applications of oil spill detection using passive UV imaging have not been as developed as other methods. In order to clarify some existing questions on oil spill detection using passive UV remote sensing technology, this paper discusses the needs of thin oil film detection, examines the feasibility of thin oil film detection using passive UV imaging through field experiments under controlled conditions and validates it with the UV imagery derived from the airborne visible/infrared imaging spectrometer (AVIRIS) observation of the Deepwater Horizon oil spill. Two types of fully automatic models are designed to extract the thin oil films on the water surface: (1) a binary classification model based on an adaptive threshold; (2) an unsupervised image segmentation model based on image clustering and greyscale histogram analysis. The two models are tested on the UV imagery obtained through both field experiments and AVIRIS observations. The results indicate that the binary classification model can extract the thin oil films with reasonable accuracy under stable imaging conditions, while the unsupervised image clustering model can robustly detect the thin oil films at the cost of higher computational complexity. These results infer that passive UV imaging is an effective way to detect thin oil films and could be applied to provide early warning at the beginning stage of oil spills and reduce further damage. It may also be applied as a supplementary method for oil spill detection to achieve comprehensive oil spill monitoring. Numéro de notice : A2023-163 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12439 Date de publication en ligne : 09/02/2023 En ligne : https://doi.org/10.1111/phor.12439 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102866 [article]

A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data / Linhua Ma in Science of the total environment, vol 859 n° 1 (February 2023)  

Public [article]  inScience of the total environment > vol 859 n° 1 (February 2023) . - n° 159917 Titre : A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data Type de document : Article/Communication Auteurs : Linhua Ma, Auteur ; Yuanlai Cui, Auteur ; Bo Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 159917 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse spatio-temporelle [Termes IGN] Chine [Termes IGN] Corée [Termes IGN] données multisources [Termes IGN] Etats-Unis [Termes IGN] humidité du sol [Termes IGN] image à haute résolution [Termes IGN] image infrarouge [Termes IGN] Italie [Termes IGN] méthane [Termes IGN] modélisation [Termes IGN] réflectance du sol [Termes IGN] rizière [Termes IGN] système d'information géographique [Termes IGN] variation saisonnière Résumé : (auteur) Quantification of regional methane (CH4) gas emission in the paddy fields is critical under climate warming. Mechanism models generally require numerous parameters while empirical models are too coarse. Based on the mechanism and structure of the widely used model CH4MOD, a GIS-based Regional CH4 Emission Calculation (GRMC) method was put forward by introducing multiple sources of remote sensing images, including MOD09A1, MOD11A2, MOD15A2H as well as local water management standards. The stress of soil moisture condition (f(water)) on CH4 emissions was quantified by calculating the redox potential (Eh) from days after flooding or falling dry. The f(water)-t curve was calculated under different exogenous organic matter addition. Combining the f(water)-t curve with local water management standards, the seasonal variation of f(water) was obtained. It was proven that f(water) was effective in reflecting the regulation role of soil moisture condition. The GRMC was tested at four Eddy Covariance (EC) sites: Nanchang (NC) in China, Twitchell (TWT) in the USA, Castellaro (CAS) in Italy and Cheorwon (CRK) in Korea and has been proven to well track the seasonal dynamics of CH4 emissions with R2 ranges of 0.738–0.848, RMSE ranges of 31.94–149.22 mg C/m2d and MBE ranges of −66.42- -14.79 mg C/m2d. The parameters obtained in Nanchang (NC) site in China were then applied to the Ganfu Plain Irrigation System (GFPIS), a typical rice planting area of China, to analyse the spatial-temporal variations of CH4 emissions. The total CH4 emissions of late rice in the GFPIS from 2001 to 2013 was in the range of 14.47–20.48 (103 t CH4-C). Ts caused spatial variation of CH4 production capacity, resulting in the spatial variability of CH4 emissions. Overall, the GRMC is effective in obtaining CH4 emissions from rice fields on a regional scale. Numéro de notice : A2023-015 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.scitotenv.2022.159917 Date de publication en ligne : 04/11/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.159917 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102133 [article]

Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 399 - 41 Titre : Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran Type de document : Article/Communication Auteurs : Sahand Seraj, Auteur ; Mahmoud Reza Delavar, Auteur Année de publication : 2022 Article en page(s) : pp 399 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie géologique [Termes IGN] classification floue [Termes IGN] entropie de Shannon [Termes IGN] forage [Termes IGN] granulométrie (pétrologie) [Termes IGN] hydrocarbure [Termes IGN] incertitude géométrique [Termes IGN] Iran [Termes IGN] prospection minérale [Termes IGN] sous ensemble flou [Termes IGN] système d'information géographique Résumé : (auteur) It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock, reservoir rock, trap and seal rock. In order to overcome such attributes with uncertainties, a number of soft computing methods are used. Information granules could be provided by the Rough Fuzzy Set Granulation (RFSG) with a thorough quality evaluation. This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems. This paper is an endeavor to recommend a Geospatial Information System (GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing. The model used the RFSG for the attribute reduction by a Decision Logic language (DL-language). The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty. In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification, the fuzzy entropy and fuzzy cross-entropy are applied. The proposed RFSG model applied for 62 structures as the training data, average fuzzy entropy has been calculated as 0.85, whereas the average fuzzy cross-entropy has been calculated 0.18. As it can be discerned, just seven structures had cross-entropies greater than 0.1, while three structures were larger than 0.3. It is implied that the precision of the proposed model is about 89%. The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values. Numéro de notice : A2022-724 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2021.2020600 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1080/10095020.2021.2020600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101667 [article]

Decision fusion of deep learning and shallow learning for marine oil spill detection / Junfang Yang in Remote sensing, vol 14 n° 3 (February-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 3 (February-1 2022) . - n° 666 Titre : Decision fusion of deep learning and shallow learning for marine oil spill detection Type de document : Article/Communication Auteurs : Junfang Yang, Auteur ; Yi Ma, Auteur ; Yabin Hu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 666 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de fusion [Termes IGN] analyse multiéchelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] hydrocarbure [Termes IGN] image hyperspectrale [Termes IGN] marée noire [Termes IGN] milieu marin [Termes IGN] pollution des mers [Termes IGN] précision de la classification [Termes IGN] sous ensemble flou [Termes IGN] surveillance écologique [Termes IGN] transformation en ondelettes Résumé : (auteur) Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively. Numéro de notice : A2022-125 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14030666 Date de publication en ligne : 30/01/2022 En ligne : https://doi.org/10.3390/rs14030666 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99688 [article]

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)  

Permalink

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

Permalink

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])  

Permalink

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)  

Permalink

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)  

Permalink

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

Permalink

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

Permalink

Ground displacement measurements / Louis-Marie Gauer (2019)

Permalink

Monitoring of marine pollution / Houma Bachari Fouzia (2019)  

Permalink

Suivi géodésique des réservoirs souterrains / Eric Henrion (2019)  

Permalink