Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 189-193, 2013
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W2/189/2013/
doi:10.5194/isprsarchives-XL-7-W2-189-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
29 Oct 2013
Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon
B. Osmanoglu1, C. Ozkan2, and F. Sunar3 1USRA – NASA Goddard Space Flight Center, USA
2Erciyes University, Kayseri, Turkey
3Istanbul Technical University, Istanbul, Turkey
Keywords: Oil spill, SAR Abstract. After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea.

In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value.

The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction.

We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

Conference paper (PDF, 1040 KB)


Citation: Osmanoglu, B., Ozkan, C., and Sunar, F.: Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W2, 189-193, doi:10.5194/isprsarchives-XL-7-W2-189-2013, 2013.

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