DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

TL;DR

DGNet is a novel oil spill segmentation framework for SAR images that leverages the intrinsic distribution of backscatter values, enabling accurate segmentation with limited training data.

Contribution

The paper introduces DGNet, a segmentation network that incorporates the physical distribution of SAR backscatter values, improving efficiency and accuracy in oil spill detection.

Findings

DGNet achieves accurate segmentation with limited data.

Incorporating intrinsic distribution improves model performance.

Experimental results validate effective oil spill segmentation.

Abstract

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.