A combination of airborne and satellite-based remote sensing is currently used for operational oil-spill monitoring worldwide. Spaceborne satellite-based synthetic aperture radar (SAR) images provide 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 of 2? 14 m/s) and good coverage.? In this paper by using a neural network the oil spill regions have been extracted in the radar image.
- Principal Component Analysis
- Local binary pattern and shape features
- KNN and FNN classifier
Drawbacks of Existing method
- High Computational load and poor discriminatory power.
- LBP doesn’t differentiate the local texture region.
- FNN is slow training for a 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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