  Successful implementation of oil spill segmentation in Synthetic Aperture
Radar (SAR) images is vital for marine environmental protection. In this paper,
we develop an effective segmentation framework named DGNet, which performs oil
spill segmentation by incorporating the intrinsic distribution of backscatter
values in SAR images. Specifically, our proposed segmentation network is
constructed with two deep neural modules running in an interactive manner,
where one is the inference module to achieve latent feature variable inference
from SAR images, and the other is the generative module to produce oil spill
segmentation maps by drawing the latent feature variables as inputs. Thus, to
yield accurate segmentation, we take into account the intrinsic distribution of
backscatter values in SAR images and embed it in our segmentation model. The
intrinsic distribution originates from SAR imagery, describing the physical
characteristics of oil spills. In the training process, the formulated
intrinsic distribution guides efficient learning of optimal latent feature
variable inference for oil spill segmentation. The efficient learning enables
the training of our proposed DGNet with a small amount of image data. This is
economically beneficial to oil spill segmentation where the availability of oil
spill SAR image data is limited in practice. Additionally, benefiting from
optimal latent feature variable inference, our proposed DGNet performs accurate
oil spill segmentation. We evaluate the segmentation performance of our
proposed DGNet with different metrics, and experimental evaluations demonstrate
its effective segmentations.
