Multifeature Semantic Complementation Network for Marine Oil Spill Localization and Segmentation Based on SAR Images

Abstract

Marine oil spill causes severe damage to the marine ecological environment. Synthetic aperture radar (SAR) is widely used in marine oil spill detection due to its all-day and all-weather advantages. However, long stripe shape oil spill areas make it challenging to extract the oil spills effectively. A multi-feature semantic complementation network (MFSCNet) is proposed for oil spill localization and segmentation of SAR images in one framework to address these problems. The long strip shape interference of oil spill is reduced by extracting intensity and damping ratio characters from non-polarimetric features and entropy, anisotropy, and mean scattering angle from polarization features to form a multi-feature SAR image. Then, the backbone feature network and feature fusion module are used for feature extraction. The decoupled head and the proposed oil spill semantic segmentation head are used for localization and semantic segmentation tasks, respectively. Also, the semantic complementation module is used in the training phase. It combines the results of localization and semantic segmentation to obtain complementation boxes for interactive iterative updating of the model parameters to enhance the detection accuracy of localization boxes. The effectiveness of the proposed model is demonstrated based on a lot of Sentinel-1 oil spill data compared with other state-of-the-art methods. The code of this work will be available at https://github.com/fjc1575/Marine-Oil-Spill/tree/ main/MFSCNet for the sake of reproducibility.

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“…Another study by J. Fan et al [41] built a framework using a multi-feature semantic complementation network (MFSCNet) for oil spill localization and segmentation of SAR images obtained via Sentinel-1 satellite data. The study by Mahmoud, A.S. et al [42] applies a novel deep learning UNET model based on the Dual Attention Model (DAM).…”