Improvement of Oil Spill Mapping from Satellite Image Using Directional Median Filtering with Articicial Neural Network

S.H. Park, H.S. Jung
University of Seoul,

Keywords: oil spill mapping, directional median filter, artificial neural network, satellite image


Oil spill accidents in marine environments have a massive impact on ecosystems. Various methods have been developed to detect oil spills using high-resolution optical imagery. However, ocean waves caused by heavy winds occurring in the accident area cause sun glint in the image, and this severely impedes the ability to detect the oil spill area. The objective of this study was to detect oil spill areas from high-resolution optic images using the artificial neural network (ANN) through effective suppression of severe sun glint effects. To enable this, a directional median filter (DMF) was adapted. The oil spill area was classified with accuracies of approximately 98.12% and 89.56% using the receiver operating characteristic (ROC) curve and the probability of detection (POD) measurements, respectively.