XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/2: SAR INTERFEROMETRY]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: TESTING THE GENERALIZATION EFFICIENCY OF OIL SLICK CLASSIFICATION ALGORITHM USING MULTIPLE SAR DATA FOR DEEPWATER HORIZON OIL SPILL C. Ozkan, B. Osmanoglu, F. Sunar, G. Staples, K. Kalkan, F. Balik Sanli

Document type:: Multivolume work

Structure type:: Chapter

2012 atures of oil fect. Journal zy approach nce and Re- ntitative Re- ional, Vol. 1, onetic waves 'hilosophical n by satellite 1), pp. 1-13. rsen, O. and ing of an oil illetin 62(2), 'alabresi, G., ers-sar data. ns on 38(5), ing wind his- ensing 20(1), and Trivero, zes. Interna- 3566. /. and Lange, r by oceanic ir theoretical ), pp. 52-70. of terrasar-x and Remote hine learning es. Machine and Garrett, ity waves by on with a mi- c Technology 2009. On the ion. Interna- 602. fferent semi- ase study in ARSeL Sym- ll detection in note Sensing, istent, hierar- AL OF GEO- 3741-8743. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia TESTING THE GENERALIZATION EFFICIENCY OF OIL SLICK CLASSIFICATION ALGORITHM USING MULTIPLE SAR DATA FOR DEEPWATER HORIZON OIL SPILL C. Ozkan!, B. Osmanoglu', F. Sunar’, G. Staples”, K. Kalkan’, F. Balik Sanl1° ! Erciyes University, Engineering Faculty, Geodesy and Photogrammetry Engineering Dept., 38039 Kayseri, Turkey, cozkan@erciyes.edu.tr ? University of Alaska - Fairbanks, P.O. Box 757320, Fairbanks AK 99775, bosmanoglu@alaska.edu ? Istanbul Technical University, Civil Engineering Faculty, Geomatics Engineering Dept., 34469 Maslak Istanbul, Turkey, fsunar@itu.edu.tr, kalkank@itu.edu.tr ^ MDA,13800 Commerce Parkway, Richmond, V7S 1L5, Canada, gstaples@mdacorporation.com * Yildiz Technical University, Civil Engineering Faculty, Geodesy and Photogrammetry Engineering Dept., Davutpasa Campus, 34220 Esenler, Istanbul, Turkey, fbalik@yildiz.edu.tr Commission VII, WG VII/2 KEYWORDS: Marine pollution, Oil spill classification, Generalization, SAR ABSTRACT: Marine oil spills due to releases of crude oil from tankers, offshore platforms, drilling rigs and wells, etc. are seriously affecting the fragile marine and coastal ecosystem and cause political and environmental concern. A catastrophic explosion and subsequent fire in the Deepwater Horizon oil platform caused the platform to burn and sink, and oil leaked continuously between April 20th and July 15th of 2010, releasing about 780,000 m? of crude oil into the Gulf of Mexico. Today, space-borne SAR sensors are extensively used for the detection of oil spills in the marine environment, as they are independent from sun light, not affected by cloudiness, and more cost-effective than air patrolling due to covering large areas. In this study, generalization extent of an object based classification algorithm was tested for oil spill detection using multiple SAR imagery data. Among many geometrical, physical and textural features, some more distinctive ones were selected to distinguish oil and look alike objects from each others. The tested classifier was constructed from a Multilayer Perception Artificial Neural Network trained by ABC, LM and BP optimization algorithms. The training data to train the classifier were constituted from SAR data consisting of oil spill originated from Lebanon in 2007. The classifier was then applied to the Deepwater Horizon oil spill data in the Gulf of Mexico on RADARSAT-2 and ALOS PALSAR images to demonstrate the generalization efficiency of oil slick classification algorithm. INTRODUCTION The backscatter energy level for oil-spilled areas received in | E. ; SAR systems is too low since the oil dampens the capillary Marine oil spills, a form of pollution, caused by releases of waves of the sea surface. However, there is a serious problem crude oil from tankers, offshore platforms, drilling rigs and that the other natural phenomenas also dampen the short wells, etc. can seriously affect the fragile marine and coastal waves and create dark areas on the sea surface due to ecosystem and cause political and environmental concern. suspension of Bragg scattering mechanism depending to The amount of oil spilled annually worldwide has been ocean and/or atmospheric conditions, (Solberg et al. 2007). estimated at more than 4.5 million tons (Bava et al., 2002). Thus, the dark image regions of which the probabilities of The Deepwater Horizon oil spill (also referred to as the Gulf being either oil spill or alike are high must be segmented. of Mexico oil spill) happened on April 20, 2010, is the largest These segmented parts of original images are used to obtain accidental marine oil spill in the history of the USA the features of shape, contrast and textural characteristics. In petroleum industry. The resulting oil slick quickly expanded terms of these features, some classification algorithms based to cover hundreds of square miles of ocean surface, being a on statistical, neural, fuzzy, rule based, boosting algorithms threat to marine life and adjacent coastal wetlands and to the etc. are used for identification of the dark areas in a manner Gulfs fishing and tourism industries. of binary classification, i.e. oil or alike (Fiscella et al., 2000; ; Del Frate et al., 2000; Solberg et al., 1999; Keramitsoglou et For rapid emergency response activities, four basic issues; i) al., 2006; Ramalho and Medeiros, 2006). prevention ii) alarm, iii) monitoring and iv) damage quantification, should be planned carefully (Bava et al., 2002). In order to lessen its effect, the improvement of its detection and continuous monitoring are the most important In this study, segmentation of dark objects was done by eCognition software. Among many geometrical, physical and C ; textural features, some more distinctive ones were selected to issues to effectively plan countermeasures responses. Today, distinguish oil and look alike objects from each others. The PURE ANR Being d am pr pow tested classifier was constructed from a Multilayer Perceptron over He Past decade Symmenc Aperture Radar (SAR) Artificial Neural Network trained by Artificial Bee Colony satellites are often preferred to optical sensors due to the all (ABC), Levenbera-Marguardt (LM). and. Backpropagntion Weather and ell dav camaialifies aud Dens used tQ detect th e (BP) optimization algorithms. The training data to train the oil spills discharged into the sea with sufficient accuracies classifier were constituted ‘from SAR data consisting of oil Cabas à à 2007) Oir Spi doipotion procedures m SAR spill originated from Lebanon in 2007. The classifier was data generally comprise segmentation, feature extraction and then applied to the Deepwater Horizon oil spill data in the classification stages (Solberg et al, 2007; Brekke and Gulf of Mexico ‘on RADARSAT-2 and ALOS PALSAR Solberg, 2005). 67

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