On the Performance of Filters for Reduction of Speckle Noise in SAR Images off the Coast of the Gulf of Guinea
This work addresses the need for improved oil spill detection in West Africa to mitigate environmental impacts, but it is incremental as it applies existing filters to a specific regional dataset.
The paper evaluated the performance of mean and median filters for reducing speckle noise in SAR images used for oil spill monitoring in the Gulf of Guinea, finding that these filters are effective preprocessing steps for image processing algorithms.
Synthetic Aperture Radar (SAR) imagery to monitor oil spills are some methods that have been proposed for the West African sub-region. With the increase in the number of oil exploration companies in Ghana (and her neighbors) and the rise in the coastal activities in the sub-region, there is the need for proper monitoring of the environmental impact of these socio-economic activities on the environment. Detection and near real-time information about oil spills are fundamental in reducing oil spill environmental impact. SAR images are prone to some noise, which is predominantly speckle noise around the coastal areas. This paper evaluates the performance of the mean and median filters used in the preprocessing filtering to reduce speckle noise in SAR images for most image processing algorithms.