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Poster Open Access

Detection of oil spills in inland lake using multi-spectral satellite images

Mantsis, Damianos Florin; Bakratsas, Marios; Vlachos, Konstantinos; Moumtzidou, Anastasia; Gialampoukidis, Ilias; Vrochidis, Stefanos; Kompatsiaris, Ioannis

Satellite images play a crucial role in monitoring Earth’s oceans, especially when it comes to oil spills. Traditionally, detection methods use Synthetic Aperture Radar (SAR) images that allow the detection of oil spills independent of clouds or daylight. However, SAR based methods are limited by wind conditions, as well as, look-alikes. Multispectral satellite images are perfect tools to fill this gap given that they allow the detection of pollution when weak or strong winds do not allow the use of SAR images. For this, a case of oil spill contamination is investigated in an inland lake in northern Greece using Sentinel-2 and PlanetScope multi-spectral images. This case is characterized by a small sample of known oil spills, making this study even more challenging. First, we implement different atmospheric corrections to acquire the remote sensing reflectance for the multispectral bands. Our sensitivity analysis shows that the detection capability for oil spills in not constrained only to the visible (VIS) part of the spectrum, but also extends to the Near Infrared (NIR), as well as, the Short Wavelength Infrared (SWIR). Among these, the NIR (833 nm) and Narrow NIR (865 nm) seem to have the largest sensitivity to fresh water oil spills. Additionally, the oil spills investigated tend to enhance the remote sensing reflectance for the NIR and SWIR part of the spectrum, but reduce it for the VIS bands, with the exception of Red (665 nm), which has a more ambiguous behavior. Given the reduced known oil spill cases (just two) for this study a pixel based machine learning approach is implemented instead of an object based one. Furthermore, the size of the oil spills will determine the choice of the bands, given that low resolution bands tend to reduce the pixel sample, and high resolution bands are limited by their availability (only four are available). Finally, the chosen bands are feed into a Deep Neural Network with two hidden layers for processing and the optimal hyperparameters are investigated. Despite the limited oil spill sample, the results are encouraging, showing a good detection capability.

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