• M. Sornam Department of Computer Science, University of Madras, Chepauk, Chennai, India




SAR, Artificial Neural Network (ANN), Segmentation, Look-Alikes, Oil Spills


Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.


Download data is not yet available.


A. Akkartal, F. Sunara, The Usage Of Radar Images In Oil Spill Detection, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing 2008.

Akram A. Moustafa, Performance Evaluation of Artificial Neural Networks for Spatial Data Analysis, Contemporary Engineering Sciences, Vol. 4, 2011, no. 4, 149 – 163.

AlaaSheta, MouhammdAlkasassbeh, Malik Braik and Hafsa Abu AyyashDetection of Oil Spills in SAR Images using Threshold Segmentation Algorithms, International Journal of Computer Applications (0975 - 8887) Volume 57 - No. 7, November 2012.

Anne H. Schistad Solberg, GeirStorvik, Rune Solberg, and EspenVolden, Automatic Detection of Oil Spills in ERS SAR Images, IEEE Transactions On Geoscience And Remote Sensing, Vol. 37, No. 4, July 1999. DOI: https://doi.org/10.1109/36.774704

Camilla Brekkea, Anne H.S. Solberg, Oil spill detection by satellite remote sensing, Remote Sensing of Environment 95 (2005) 1–13, 2004 Elsevier Inc. DOI: https://doi.org/10.1016/j.rse.2004.11.015

CEARAC SAR images database: http://cearac.poi.dvo.ru/en//db/

F. Nirchio, S. Di Tomaso, W. Biamino, E. Parisato, P. Trivero, A. Giancaspro,Oil Spills Automatic Detection From Sar Images, Proc. of the 2004 Envisat& ERS Symposium, Salzburg, Austria 6-10 September 2004. DOI: https://doi.org/10.1080/01431160512331326558

Konstantinos N. Topouzelis, Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms, Review Sensors 2008, 8, 6642-6659. DOI: https://doi.org/10.3390/s8106642

Konstantinoskarantzalos, DemetreArgialas, Automatic detection and tracking of oil spills in SAR imagery with level set segmentation, International Journal of Remote Sensing, 2008. DOI: https://doi.org/10.1080/01431160802175488

Krishna Kant Singh1 , Akansha Singh2, A Study Of Image Segmentation Algorithms For Different Types Of Images, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 5,September 2010.

K. Topouzelis, V. Karathanassi, P. Pavlakis and D. Rokos, Oil Spill Detection: SAR Multi-scaleSegmentation & Object Features Evaluation, Proceedings of SPIE Vol. 4880 (2003) © 2003. DOI: https://doi.org/10.1117/12.462518

Mahinda P. Pathegama and ÖzdemirGöl, Edge-end Pixel Extraction for Edge-based Image Segmentation, International Journal of Computer, Information, Systems and Control EngineeringVol:1 No:2, 2007.

RadhikaViswanathan and PadmavathiGanapathi, Feature Extraction and classification of Oil spill in SAR Imagery, IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 2, and September 2011.




How to Cite

Sornam, M. (2017). OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK . International Journal of Engineering Technologies and Management Research, 4(11), 1–10. https://doi.org/10.29121/ijetmr.v4.i11.2017.117