Random Articles
Reseach Article
Detection of Oil Spills in SAR Images using Threshold Segmentation Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 57 - Number 7 |
| Year of Publication: 2012 |
| Authors: Alaa Sheta, Mouhammd Alkasassbeh, Malik Braik, Hafsa Abu Ayyash |
10.5120/9125-3291
|
Alaa Sheta, Mouhammd Alkasassbeh, Malik Braik, Hafsa Abu Ayyash . Detection of Oil Spills in SAR Images using Threshold Segmentation Algorithms. International Journal of Computer Applications. 57, 7 ( November 2012), 10-15. DOI=10.5120/9125-3291
Abstract
Identification of potential oil spills on Synthetic Aperture Radar (SAR) satellite images is a complex process. Oil companies, as well as the coast guard have tested a whole range of methods for monitoring and detection of possible oil spills. These methods are found to be expensive, complex and require high processing power and time. In this paper, an oil spill detection method is proposed. The method consists of four main stages, namely: 1) Image enhancement; 2) Image segmentation 3) feature extraction; and 4) Object recognition of the segmented objects as oil spills or look-likes. The algorithm was trained on a large number of Synthetic Aperture Radar (SAR) images. The proposed thresholding algorithm can be considered an alternative to manual inspection for large ocean areas. Promising results and high detection rates for the oil spills have been achieved.
References
- Hafssa A. AbuAyyash. Improving oil spill detection using remote sensing techniques, m. sc. of computer science.
- R´egia T. S. Ara´ujo, F´atima N. S. de Medeiros, Rodrigo C. S. Costa, R´egis C. P. Marques, Rafael B. Moreira, and Jilseph L. Silva. Locating oil spill in SAR images using wavelets and region growing. In IEA/AIE'2004: Proceedings of the 17th international conference on Innovations in applied artificial intelligence, pages 1184–1193. Springer Springer Verlag Inc, 2004.
- Malik Braik, Alaa Sheta, and Aladdin Ayesh. Particle swarm optimisation enhancement approach for improvingimage quality. Int. J. Innovative Computing and Applications, 1(2):138–145, 2007.
- Lena Chang, Z. S. Tang, S. H. Chang, and Yang-Lang Chang. A region-based GLRT detection of oil spills in SAR images. Pattern Recogn. Lett. , 29(14):1915–1923, 2008.
- I. El-Feghi, N. Adem, M. A. Sid-Ahmed, and M. Ahmadi. Improved co-occurrence matrix as a feature space for relative entropy-based image thresholding. In Proceedings of IEEE Computer Society, pages 314–320. Computer Graphics, Imaging and Visualisation, 2007.
- F. D. Frate, A. Petrocchi, J. Lichtenegger, and G. Calabresi. Neural networks for oil spill detection using ERS SAR data. IEEE Transaction Geosc. Rem. Sens. , 38:2282–2287, 2000.
- S. Galicia. Galicia (Spain), November 2002-April 2003. current URL is http://earth. esa. int/ew/oil slicks/galicia, 2008.
- R. Gonzalez and R. Woods. Digital Image Processing. Prentice Hall, 3rd Edition, 2008.
- R. Gonzalez, R. Woods, and S. Eddins. Digital Image Processing using MATLAB. Prentice Hall,2nd Edition, Upper Saddle River, NJ Jensen, 2004.
- M. Jha, J. Levy, and Y. Gao. Advanced in remote sensing for oil spill disaster management: State of the art sensors technology for oil spill surveillance. Sensors Journal, 8(13):236 – 255, 2008.
- Iphigenia Keramitsoglou, Constantinos Cartalis, and Chris T. Kiranoudis. Automatic identification of oil spills on satellite images. Environ. Model. Softw. , 21(5):640–652, May 2006.
- J. Li. Spill management for the toronto AOC. Technical report, 2002.
- Lin Li, Susan L. Ustin, and Mui Lay. Application of AVIRIS data in detection of oil-induced vegetation stress and cover change at jornada, new mexico. Remote Sensing of Environment, 94(1):1–16, January 2005.
- A. Montali, G. Giacinto, M. Migliaccio, and A. Gambardella. Supervised pattern classification techniques for oil spill classification in SAR images: Preliminary results. In European Space Agency, pages 123–127. Advances in SAR Oceanography from Envisat and ERS Missions, 2006.
- J. C. Prados. Chemical dispersants and bioremediation for the treatment of oil spills. Technical report, 2003.
- A. Solberg, C. Brekke, E. Volden, and P. Husoy. Oil spill detection in radarsat and envisat SAR images. IEEE Transactions on Geoscience and remote Sensng, 45(3):746–755, 2007.
- A. Solberg, T. Dokken, and R. Solberg. Automatic detection of oil spills in envisat, radarsat and ERS SAR images. IEEE Geoscience and Remote Sensing Symposium, 4(3):2747–2749, 2003.
- A. H. Solberg, G. Storvik, R. Solberg, and E. Volden. Automatic detection of oil spills in ERS SAR images. In IEEE Transactions on Geoscience and Remote Sensing, volume 37, pages 1916 – 1924, 1999.
- Konstantinos Topouzelis, Karathanassi Vassilia, Pavlakis Petros, and Rokos Demetrius. Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes. Geocarto International, 24(3):179–191, 2009.
- Y. Tsai. A new approach for image thresholding under uneven lighting conditions. In Proceedings of the IEEE/ACIS International Conference on Computer and Information Science, 11-13 July, pages 123 – 127. Computer and Information Science, 2007.
- V. Turkar. Polarimetric sar image classification by using artificial neural network. In Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ICWET '10, pages 48–52, New York, NY, USA, 2010. ACM.
- Ying Yu, Bin Wang, and Liming Zhang. Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images. Neurocomput. , 74(11):2008–2017, May 2011.
Index Terms
Keywords