PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Monitoring and identification of marine oil spills using advanced synthetic aperture radar images

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Czasopismo
Rocznik
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.
  • [2] FRIEDMAN K.S., PICHEL W.G., CLEMENTE-COLON P., XIAOFENG LI, GoMEx – an experimental GIS system for the Gulf of Mexico region using SAR and additional satellite and ancillary data, [In] IEEE International Geoscience and Remote Sensing Symposium, IGARSS ’02, Vol. 6, 2002, pp. 3343–3345.
  • [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.
  • [5] WILLIAMS A., LAWRENCE G., The role of satellite seep detection in exploring the South Atlantic’s ultra deep water, [In] Schumacher D., LeSchack L.A. [Eds.], Surface Exploration Case Histories: Applications of Geochemistry, Magnetics and Remote Sensing, AAPG Studies in Geology No. 48, SEG Geophysical References Series No. 11, 2002, pp. 327–344.
  • [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.
  • [13] SOLBERG A.H.S., BREKKE C., HUSØY P.O., Oil spill detection in radarsat and envisat SAR images, IEEE Transactions on Geoscience and Remote Sensing 45(3), 2007, pp. 746–755.
  • [14] FISCELLA B., GIANCASPRO A., NIRCHIO F., PAVESE P., TRIVERO P., Oil spill detection using marine SAR images, International Journal of Remote Sensing 21(18), 2000, pp. 3561–3566.
  • [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.
  • [20] RAMOS-FUERTES A., MARTI-CARDONA B., BLADÉ E., DOLZ J., Envisat/ASAR images for the calibration of wind drag action in the Doñana wetlands 2D hydrodynamic model, Remote Sensing 6(1), 2014, pp. 379–406.
  • [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.
  • [22] SHENGLI HUANG, POTTER C., CRABTREE R.L., HAGER S., GROSS P., Fusing optical and radar data to estimate sagebrush, herbaceous and bare ground cover in Yellowstone, Remote Sensing of Environment 114(2), 2010, pp. 251–264.
  • [23] FINGAS M.F., BROWN C.E., Review of oil spill remote sensing, Spill Science and Technology Bulletin 4(4), 1997, pp. 199–208.
  • [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.
  • [29] WACKERMAN C.C., Digital SAR image formation, [In] CARSEY F.D. [Ed.], Microwave Remote Sensing if Sea Ice, Geophysical Monograph 68, Washington, 1992, pp. 105–110.
  • [30] HASSINI A., DÉJEAN S., BENABADJI N., HASSINI N., BELBACHIR A.H., Forest fires smoke monitoring from sea-viewing wide field-of-view sensor images, Optica Applicata 38(4), 2008, pp. 737–754.
  • [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.
  • [32] ÖZKAN C., SUNAR F., Comparisons of different semi-automated techniques for oil-spill detection: a case study in Lebanon, 27th EARSel Symposium, June 4–7, 2007, Bolzano, Italy.
  • [33] RUSS J.C., The Image Processing Handbook, CRC Press, Boca Raton, United States,1992, p. 445.
  • [34] ESPINDOLA G.M., CAMARA G., REIS I.A., BINS L.S., MONTEIRO A.M., Parameter selection for region- -growing image segmentation algorithms using spatial autocorrelation, International Journal of Remote Sensing 27(14), 2006, pp. 3035–3040.
  • [35] YUANMING SHU, LI J., YOUSIF H., GOMES G., Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring, Remote Sensing of Environment 114(9), 2010, pp. 2026–3035.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-908079a7-68d7-4967-b391-56c02e3c7158
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.