Deep Learning for Monitoring and Managing Oil Spill in Underwater Sensor Networks: Enabling Technologies

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dc.contributor.authorGoyal, Nitin
dc.date.accessioned2026-03-27T09:42:42Z
dc.date.available2026-03-27T09:42:42Z
dc.date.issued2026
dc.description.abstractEnvironmental problems can significantly impact people’s lives and must be addressed. The oil spill is one of the biggest applications of underwater wireless sensor networks (UWSNs) that threaten aquatic life, so it is essential to focus on it. Oil spill count has risen in recent years because of the rise in shipping and marine transportation industries. Timely and accurate detection of oil spills can improve the response process and get the necessary resources to the affected areas more efficiently. This work was motivated by the fact that oil spill detection is critical to maritime protection. Since oil spill detection is a complex process, it faces various challenges. One of the biggest challenges is similar visual appearance which makes it difficult to identify an oil spill and a similar oil slick in synthetic aperture radar (SAR) imagery. Various radar images are frequently utilized for this purpose. Despite being widely used for earth observation, SAR images have noisy and illegible image quality, which makes classification challenging. Previously, detecting and classifying SAR images required manual involvement, which made the process time consuming. Therefore, researchers focused on automating such tasks by incorporating deep learning (DL) techniques. Numerous papers proposed the applications of DL in various radar images for oil spill monitoring but faced multiple problems. This study aims to thoroughly investigate oil spills and their detection techniques, utilizing DL techniques applied to various radar images. These studies have originated in diverse nations with distinct environments. However, compared to other types of radar images, synthetic aperture radar (SAR) images are more effective in pinpointing the location of oil spills. However, they are also rather complex.
dc.identifier.urihttp://cuh.ndl.gov.in/handle/123456789/1855
dc.language.isoen
dc.titleDeep Learning for Monitoring and Managing Oil Spill in Underwater Sensor Networks: Enabling Technologies
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