SRCNet: Seminal Image Representation Collaborative Network for Oil Spill Segmentation in SAR Imagery

Abstract

Effective oil spill segmentation in synthetic aperture radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation contributes to effective learning for accurate oil spill segmentation. In this article, we propose an effective oil spill segmentation network named SRCNet, which is constructed by leveraging seminal SAR image representation to empower the learning capability of the proposed segmentation network for accurate oil spill segmentation. Specifically, the image representation utilized in our proposed SRCNet originates from SAR imagery, modeling with the internal characteristics of oil spill SAR image data, which therefore promotes effective learning for accurate oil spill segmentation in the training process. Besides, to conduct enhanced oil spill segmentation, we construct the proposed SRCNet with a pair of deep neural nets that work in a competition manner, where one neural net strives to produce accurate oil spill segmentation maps by drawing samples from the collaborated seminal image representation, while the other tries its best to distinguish between the produced and the true segmentations. It is the competition and the image representation collaborated that drives the proposed SRCNet to operate accurate oil spill segmentation efficiently with small amount of training data. This establishes an economical and efficient way for oil spill segmentation. Additionally, to further improve the segmentation performance of the proposed SRCNet, a regularization term that penalizes the segmentation loss is devised, which encourages the produced segmentation to approach the ground-truth segmentation, promoting the segmentation capability of the proposed SRCNet for accurate oil spill segmentation. Experimental evaluations from different metrics validate the effectiveness of the proposed SRCNet for oil spill segmentation.