Identyfikatory
DOI
Warianty tytułu
Języki publikacji
Abstrakty
The aim of this study is to propose and test a new methodology for detection of oil spills in the world oceans from advanced synthetic aperture radar imagery embedded in ENVISAT satellite (ENVISAT-ASAR). The proposed and applied methodology includes four levels: data acquisition, dark spots detection, features extraction and dark spots classification for discrimination between oil spills and look-alikes. Level 1 contains the ENVISAT-ASAR wide swath mode data acquisition. Level 2 begins with a visual interpretation based on experience and a priori information concerning location, external information about weather conditions, differences in shape, and contrast to surroundings between oil spills and look-alikes, then filtering and segmentation. Level 3 contains extraction of features from the detected dark spots. Level 4 aim is to discriminate oil spills from look-alikes using the features extracted by means of object-based fuzzy classification. As a result, oil slicks are discriminated from look-alikes with an overall accuracy classification of 91% for oil slicks and 86% for look-alikes. Finally, to validate our results, the method has been tested by comparing the areas of the automatically detected oil spills (object-based fuzzy classification) with the areas of the manually detected oil spills (region of interest), by means of area ratios.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
433--449
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
- Institute of Maintenance and Industrial Safety, University of Oran, B.P.05, Airport Road Es-Senia, Oran, Algeria
autor
- Institute of Maintenance and Industrial Safety, University of Oran, B.P.05, Airport Road Es-Senia, Oran, Algeria
- Laboratory of Analysis and Application of Radiation LAAR, Faculty of Physics USTOMB, El M’nouer B.P.1505 Oran, Algeria
Bibliografia
- [1] GIRARD-ARDHUIN F., MERCIER G., GARELLO R., Oil slick detection by SAR imagery: potential and limitation, [In] OCEANS 2003, Proceedings, Vol. 1, 2003, pp. 164–169.
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- [3] LEIFER I., LUYENDYK B., BRODERICK K., Tracking an oil slick from multiple natural sources: Coal Oil Point, California, Marine and Petroleum Geology 23(5), 2006, pp. 621–630.
- [4] O’BREIN G.W., LAWRENCE G.M., WILLIAMS A.K., GLENN K., BARRETT A.G., LECH M., EDWARDS D.S., COWLEY R., BOREHAM C.J., SUMMONS R.E., Yampi Shelf, Browse Basin, North-West Shelf, Australia: A test-bed for constraining hydrocarbon migration and seepage rates using combinations of 2D and 3D seismic data and multiple independent remote sensing technologies, Marine and Petroleum Geology 22(4), 2005, pp. 517–549.
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- [6] PISANO A., Development of oil spill detection techniques for satellite optical sensors and their appilcation to monitor oil spill disharge in the mdditeranean sea, PhD Thesis, Department of Control and Management of Natural Resources, University of Bologna, Italy, 2011.
- [7] BREKKE C., SOLBERG A.H.S., Oil spill detection by satellite remote sensing, Remote Sensing of Environment 95(1), 2005, pp. 1–13.
- [8] ESPEDAL H.A., Detection of oil spill and natural film in the marine environment by spaceborne synthetic aperture radar, PhD Thesis, Department of Physics, University of Bergen and Nansen Environment and Remote Sensing Center, Norway, 1998.
- [9] HORNÁČEK M., WAGNER W., SABEL D., HONG-LINH TRUONG, SNOEIJ P., HAHMANN T., DIEDRICH E., DOUBKOVÁ M., Potential for high resolution systematic global surface soil moisture retrieval via change detection using Sentinel-1, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(4), 2012, pp. 1303–1311.
- [10] TOPOUZELIS K.N., Oil spill detection by SAR images: dark formation detection, feature extraction and classification algorithms, Sensors 8(10), 2008, pp. 6642–6659.
- [11] KARATHANASSI V., TOPOUZELIS K., PAVLAKIS P., ROKOS D., An object-oriented methodology to detect oil spills, International Journal of Remote Sensing 27(23), 2006, pp. 5235–5251.
- [12] SOLBERG A.H.S., STORVIK G., SOLBERG R., VOLDEN E., Automatic detection of oil spills in ERS SAR images, IEEE Transactions on Geoscience and Remote Sensing 37(4), 1999, pp. 1916–1924.
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- [15] NIRCHIO F., SORGENTE M., GIANCASPRO A., BIAMINO W., PARISATO E., RAVERA R., TRIVERO P., Automatic detection of oil spills from SAR images, International Journal of Remote Sensing 26(6), 2005, pp. 1157–1174.
- [16] TOPOUZELIS K., KARATHANASSI V., PAVLAKIS P., ROKOS D., Detection and discrimination between oil spills and look-alike phenomena through neural networks, ISPRS Journal of Photogrammetry and Remote Sensing 62(4), 2007, pp. 264–270.
- [17] BREKKE C., SOLBERG A.H.S., Classifiers and confidence estimation for oil spill detection in ENVISAT ASAR images, IEEE Geoscience and Remote Sensing Letters 5(1), 2008, pp. 65–69.
- [18] LINLIN XU, LI J., BRENNING A., A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery, Remote Sensing of Environment 141, 2014, pp. 14–23.
- [19] WAGNER W., PATHE C., SABEL D., BARTSCH A., KUNZER C., SCIPAL K., Experimental 1 km soil moisture products from ENVISAT ASAR for Southern Africa, Proceedings of ENVISAT Symposium, Montreux, Switzerland, 2007, SP-636.
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- [21] SPARWASSER N., KRAUS T., HASCHBERGER P., Multi-temporal radar image mosaic from the Mekong River Delta (ENVISAT ASAR© ESA), Status Report 2007–2013, German Remote Sensing Data Center, Oberpfaffenhofen, September 2013, pp. 64–65.
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- [24] MOUCHE A.A., HAUSER D., DALOZE J.-F., GUERIN C., Dual-polarization measurements at C-band over the ocean: results from airborne radar observations and comparison with ENVISAT ASAR data, IEEE Transactions on Geoscience and Remote Sensing 43(4), 2005, pp. 753–769.
- [25] GIRARD-ARDHUIN F., MERCIER G., COLLARD F., GARELLO R., Operational oil-slick characterization by SAR imagery and synergistic data, IEEE Journal of Oceanic Engineering 30(3), 2005, pp. 487–495.
- [26] MIHOUB Z., HASSINI A., Oil spill detection technique from RADAR and optical satellite data, The Second International Conference on Signal, Image, Vision and their Application SIVA ’13, November 18–20, 2013, Algeria, pp. 169–174.
- [27] SERTAC AKAR, MEHMET LUTFI SÜZEN, NURETDIN KAYMAKCI, Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images, Environmental Monitoring and Assessment 183(1–4), 2011, pp. 409–423.
- [28] MIHOUB Z., HASSINI A., Oil spill monitoring and classification technique from ENVISAT-ASAR data, International Conference on Engineering of Industrial Safety and Environment ICISE ’14, January 26–27, 2014, Algeria.
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- [31] MARGHANY M., RADARSAT automatic algorithms for detecting coastal oil spill pollution, International Journal of Applied Earth Observation and Geoinformation 3(2), 2001, pp. 191–196.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-908079a7-68d7-4967-b391-56c02e3c7158