4.6 Article

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Journal

ENTROPY
Volume 24, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/e24101453

Keywords

oil spill; SAR image; deep learning; attention module; gradient profile loss

Funding

  1. Youth Innovation Science and Technology Support Program of Shandong Provincial [2021KJ080]
  2. Yantai Science and Technology Innovation Development Plan Project [2021YT06000645]

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This study proposes a method that combines deep learning and image segmentation techniques with synthetic aperture radar (SAR) to monitor marine oil spills. It uses a dual attention encoding network (DAENet) to identify oil spill areas and a gradient profile (GP) loss function to improve boundary line accuracy. Experimental results show that this method performs well on different datasets.
Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder-decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas' boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.

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