A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Wandi WANG; Hui SHENG; Yanlong CHEN; Shanwei LIU; Jijun MAO; Zhe ZENG; Jianhua WAN

doi:10.1007/s00343-020-0105-7

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A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Physics | Updated：2023-05-20

- A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images
- Journal of Oceanology and Limnology Vol. 39, Issue 4, Pages: 1198-1210(2021)
- Affiliations：
  
  1.China University of Petroleum, Qingdao 266580, China
  2.National Marine Environmental Monitoring Centre, Dalian 116023, China
  3.Surveying and Mapping Institute of Shandong Province, Jinan 250102, China
- Author bio：
  
  Hui SHENG, E-mail:sheng@upc.edu.cn
- Funds：
- DOI：10.1007/s00343-020-0105-7
  CLC：
- Received：05 March 2020，
  
  Accepted：15 June 2020，
  
  Online First：13 August 2020，
  
  Published：2021-07
- Accepted：
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Wandi WANG, Hui SHENG, Yanlong CHEN, et al. A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1198-1210.
DOI：

Wandi WANG, Hui SHENG, Yanlong CHEN, et al. A fast, edge-preserving, distance-regularized model with bilateral filtering for oil spill segmentation of SAR images[J]. Journal of Oceanology and Limnology, 2021, 39(4): 1198-1210. DOI： 10.1007/s00343-020-0105-7.

摘要

Abstract

Marine oil spills are among the most significant sources of marine pollution. Synthetic aperture radar (SAR) has been used to improve oil spill observations because of its advantages in oil spill detection and identification. However

speckle noise

weak boundaries

and intensity inhomogeneity often exist in the oil spill regions of SAR imagery

which will seriously affect the accurate identification of oil spills. To enhance marine oil spill segmentation of SAR images

a fast

edge-preserving framework based on the distance-regularized level set evolution (DRLSE) model was proposed. Specifically

a bilateral filter penalty term is designed and incorporated into the DRLSE energy function (BF-DRLSE) to preserve the edges of oil spills

and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity. Two sets of RadarSat-2 SAR data were used to test the proposed method. The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution. Compared with other methods

the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy (97.83%). In addition

using an appropriate initial box boundary for the DRLSE method accelerated the global search process

improved the accuracy of oil spill segmentation

and reduced computational time. Therefore

the results suggest that the proposed framework is effective and applicable for marine oil spill segmentation.

关键词

Keywords

references

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