Image Processing for Oil Spill Detection and its Classification using Neural Networks

  Amarpal Singh Walia,  Rekha Garg,  Preetinder Kaur

Oil Spills in ocean is an increasing threat to our ecosystem. Oil Spills happen due to either leakage of oil from ships due to accidents or due to illegal release of oil in the sea by some big companies. In both cases detecting oil spill is an important because oil spills causes environmental problems and endanger the marine life. Many fishes and marine birds die due to oil spill every year. With advent of computing technology and advanced image processing techniques, automatic detection of oil spills using SAR images has become an important area of research. The many challenges in accurately classifying an oil spill from that of look-alikes are a major area of research interest. In this thesis, we have proposed a method for detecting oil spills from that of look-alikes with very high accuracy is proposed. The proposed work has implemented various image processing techniques like thresholding and image segmentation to extract oil spill from the background, and then implemented a pattern recognition system using Artificial neural network which uses a set of features extracted from SAR image of oil spill or look alike and is trained to classify oil spill more accurately than that of look-alikes.

Keywords- Synthetic aperture radar (SAR), Oil spill detection, marine pollution, feature extraction, image segmentation, Artificial neural network (ANN)

Unique Identification Number - IJEDR1503123
Page Number(s) - 1-9
Pubished in - Volume 3 | Issue 3 | September 2015
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  Amarpal Singh Walia,  Rekha Garg,  Preetinder Kaur,   "Image Processing for Oil Spill Detection and its Classification using Neural Networks", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.3, Issue 3, pp.1-9, September 2015, Available at :http://www.ijedr.org/papers/IJEDR1503123.pdf