  Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is
critical for marine oil pollution cleanup, and proper image representation is
helpful for accurate image segmentation. In this paper, we propose an effective
oil spill image segmentation network named SRCNet by leveraging SAR image
representation and the training for oil spill segmentation simultaneously.
Specifically, our proposed segmentation network is constructed with a pair of
deep neural nets with the collaboration of the seminal representation that
describes SAR images, where one deep neural net is the generative net which
strives to produce oil spill segmentation maps, and the other is the
discriminative net which trys its best to distinguish between the produced and
the true segmentations, and they thus built a two-player game. Particularly,
the seminal representation exploited in our proposed SRCNet originates from SAR
imagery, modelling with the internal characteristics of SAR images. Thus, in
the training process, the collaborated seminal representation empowers the
mapped generative net to produce accurate oil spill segmentation maps
efficiently with small amount of training data, promoting the discriminative
net reaching its optimal solution at a fast speed. Therefore, our proposed
SRCNet operates effective oil spill segmentation in an economical and efficient
manner. Additionally, to increase the segmentation capability of the proposed
segmentation network in terms of accurately delineating oil spill details in
SAR images, a regularisation term that penalises the segmentation loss is
devised. This encourages our proposed SRCNet for accurately segmenting oil
spill areas from SAR images. Empirical experimental evaluations from different
metrics validate the effectiveness of our proposed SRCNet for oil spill image
segmentation.

Computer Vision and Pattern Recognition;Machine Learning