University of Leicester
Browse
Revised Manuscript of Our TGRS Paper.pdf (2.79 MB)

DGNet: Distribution Guided Efficient Learning for Oil Spill Image Segmentation

Download (2.79 MB)
journal contribution
posted on 2023-02-13, 11:24 authored by F Chen, Heiko Balzter, F Zhou, P Ren, Huiyu Zhou

Successful implementation of oil spill segmentation in synthetic aperture radar (SAR) images is vital for marine environmental protection. In this article, 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.

Funding

10.13039/501100004543-China Scholarship Council (Grant Number: 201806450015)

10.13039/501100000738-Ph.D. Studentship from the University of Leicester

History

Author affiliation

Institute for Environmental Futures, School of Geography, Geology and Environment, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

IEEE Transactions on Geoscience and Remote Sensing

Volume

61

Publisher

Institute of Electrical and Electronics Engineers

issn

0196-2892

Copyright date

2023

Available date

2023-02-13

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC