SRCNet: Seminal Representation Collaborative Network for Marine Oil Spill Segmentation

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.

DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

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.

Oil Spill SAR Image Segmentation via Probability Distribution Modelling

Segmentation of marine oil spills in Synthetic Aperture Radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularisation term and the updated level set regularisation term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronisation of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.