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A combination of airborne and satellite-based remote sensing is currently used for operational oil-spill monitoring worldwide. Space borne satellite-based synthetic aperture radar (SAR) images provides an overview of large ocean areas, and surveillance aircraft can be directed to check possible oil-spill locations to verify the spill and catch the polluter. Oil-spill detection is most effectively performed on a large scale using SAR images due to its all-weather capabilities (given wind speeds in the range 2?14 m/s) and good coverage.? In this paper by using neural network the oil spill regions has been extracted in radar image.
- Principal Component Analysis
- Local binary pattern and shape features
- KNN and FNN classifier
Draw backs of Existing method
- High Computational load and poor discriminatory power.
- LBP doesn?t differentiate the local texture region.
- FNN is slow training for large feature set.
- Less accuracy in classification
- DRLBP and GLCM
- Neural Network classifier
- Color Space Conversion
- GLCM Features Extraction
- DRLBP (Discriminative Robust Local binary Pattern)
- NN Training and Classification
- Fuzzy c-means clustering
- DRLBP has better discriminatory power
- NN is fast and better compatible in classification.
- Low computational complexity
- Surveillance aircraft
- Oil-spill monitoring
- Matlab2014a and above versions
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[3 D. Kim, W. Moon, and Y.-S. Kim, BApplication of TerraSAR-X data for emergent oil-spill monitoring,[ IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2, pp. 852?863, Feb. 2010.
 ] I. Keramitsoglou, C. Cartalis, and C. Kiranoudis, BAutomatic identification of oil spills on satellite images,[ Environ. Model. Softw., vol. 21, no. 5, pp. 640?652, 2006
 ] D. Casciello, T. Lacavat, N. Pergolat, and V. Tramutoli, BRobust satellite techniques (RST) for oil spill detection and monitoring,[ in Proc. Int. Workshop Anal. Multi-Temporal Remote Sens. Images, 2007, DOI: 10.1109/ MULTITEMP.2007.4293040