OIL SPILL DETECTION IN THE CASPIAN SEA WITH A SAR IMAGE USING A DENSENET MODEL
Keywords: Oil Spill, Remote Sensing, Machine Learning, SAR Image, Convolutional Neural Network (CNN), DenseNet
Abstract. Hydrocarbon spills play a vital part in contaminating water resources such as the seas and oceans. Establishing a quick and accurate approach to identifying hydrocarbons is critical to addressing these pollutants' harmful effects on the aquatic environment and local residents. The use of satellite remote sensing to detect large-scale oil spills is one of the most extensively used domains for this purpose. A machine learning method with high speed and accuracy was proposed in this study to detect occurring hydrocarbon leaks in a SAR image captured by the Sentinel-1A satellite in the Caspian Sea. The suggested method is a dense-structured network of deep learning (DenseNet) that takes the required image as input and divides it into two classes, namely, oil Spill and non-oil Spill. This dense Network outperforms the standard convolutional neural network algorithm (CNN). As a result of using the Sentinel-1A SAR image of the Caspian Sea, we achieved an overall accuracy of 99.83% and a Kappa coefficient of 0.9055.