  Synthetic Aperture Radar (SAR) is the main instrument utilized for the
detection of oil slicks on the ocean surface. In SAR images, some areas
affected by ocean phenomena, such as rain cells, upwellings, and internal
waves, or discharge from oil spills appear as dark spots on images. Dark spot
detection is the first step in the detection of oil spills, which then become
oil slick candidates. The accuracy of dark spot segmentation ultimately affects
the accuracy of oil slick identification. Although some advanced deep learning
methods that use pixels as processing units perform well in remote sensing
image semantic segmentation, detecting some dark spots with weak boundaries
from noisy SAR images remains a huge challenge. We propose a dark spot
detection method based on superpixels deeper graph convolutional networks
(SGDCN) in this paper, which takes the superpixels as the processing units and
extracts features for each superpixel. The features calculated from superpixel
regions are more robust than those from fixed pixel neighborhoods. To reduce
the difficulty of learning tasks, we discard irrelevant features and obtain an
optimal subset of features. After superpixel segmentation, the images are
transformed into graphs with superpixels as nodes, which are fed into the
deeper graph convolutional neural network for node classification. This graph
neural network uses a differentiable aggregation function to aggregate the
features of nodes and neighbors to form more advanced features. It is the first
time using it for dark spot detection. To validate our method, we mark all dark
spots on six SAR images covering the Baltic Sea and construct a dark spots
detection dataset, which has been made publicly available
(https://drive.google.com/drive/folders/12UavrntkDSPrItISQ8iGefXn2gIZHxJ6?usp=sharing).
The experimental results demonstrate that our proposed SGDCN is robust and
effective.
