Mauritius oil spill detection using transfer learning approach for oil spill mapping and wind impact analysis using Sentinel-1 data Online publication date: Mon, 07-Oct-2024
by Koushik Das; Prashanth Janardhan; Harish Narayana
International Journal of Hydrology Science and Technology (IJHST), Vol. 18, No. 4, 2024
Abstract: The oil spill detection and mapping using Sentinel-1 (S-1) data for the Mauritius oil spill event have been done in this study. The convolutional neural network (CNN)-based on pre-trained models such as AlexNet, VGG-16, and VGG-19, have been used to classify the S-1 images by the transfer learning approach. The S-1 images are classified into two classes: with and without the oil spill. Then, the oil spill detection was done in the sentinel application platform (SNAP), and the oil spill mapping was done in ArcGIS. The VGG-16 network performs the best among the other pre-trained networks with an accuracy of 96.88%, precision of 95.92%, and recall of 97.92%. The impact of wind on the spreading of oil is also analysed using remote sensing and GIS techniques. It has been observed that the spreading of oil doesn't only depend on sea wind but also other environmental factors.
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