Marine Oil Spill Detection from Low-Quality SAR Remote Sensing Images

Abstract

Oil spills pose a significant threat to the marine ecological environment. The intelligent interpretation of synthetic aperture radar (SAR) remote sensing images serves as a crucial approach to marine oil spill detection, offering the potential for real-time, continuous, and accurate monitoring. This study makes valuable contributions to the field of marine oil spill detection based on low-quality SAR images, focusing on the following key aspects: (1) We thoroughly analyze the Deep SAR Oil Spill dataset, known as the SOS dataset, a prominent resource in the domain of marine oil spill detection from low-quality SAR images, and rectify identified issues to ensure its reliability. (2) By identifying and rectifying errors in the original literature that presented the SOS dataset, and reproducing the experiments to provide accurate results, benchmark performance metrics for marine oil spill detection with low-quality SAR remote sensing images are established. (3) We propose three progressive deep learning-based marine oil spill detection methods (a direct detection method based on Transformer and UNet, a detection method based on FFDNet and TransUNet with denoising before detection, and a detection method based on integrated multi-model learning) and the performance advantages of the proposed methods are verified by comparing them with semantic segmentation models such as UNet, SegNet, and DeepLabV3+. (4) We introduce a feasible, highly robust and easily scalable system architecture approach that effectively addresses practical engineering applications. This paper is an important addition to the research on marine oil spill detection from low-quality SAR images, and the proposed experimental method and performance details can provide a reference for related research.

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

“…It does this using a channel attention map and a position attention map. Finally, Dong et al [43] propose the application of three deep learning-based marine oil spill detection methods, namely, a direct detection method based on transformer and UNet, a detection method based on Fast and Flexible CNN (FFDNet) and TransUNet with denoising before detection, and a detection method based on integrated multi-model learning. The performance benefits of the proposed method are then verified by comparing them with semantic segmentation models such as UNet, SegNet, and DeepLabV3+.…”

Living on the Coast in Harmony with Natural Processes

“…After the occurrence and detection of an oil spill at sea, it is important to implement measures to mitigate its negative impacts. SAR remote sensing methods are generally the best suited for detecting oil spills after they occur [54][55][56][57]. However, mathematical modeling is a very powerful tool for managing an oil spill accident, namely, to monitor the evolution of the oil slick taking into account the spreading and weathering processes, such as evaporation, vertical dispersion, emulsification, and viscosity changes, and also for determining preventive measures [44,57].…”