Abstract
We exploit the alternating direction method of multipliers (ADMM) for developing an oil spill segmentation method, which effectively detects oil spill regions in blurry synthetic aperture radar (SAR) images. We commence by constructing energy functionals for SAR image deblurring and oil spill segmentation separately. We then integrate the two energy functionals into one overall energy functional subject to a linear mapping constraint that correlates the deblurred image and the segmentation indicator. The overall energy functional along with the linear constraint follows the form of ADMM and thus enables an effective augmented Lagrangian optimization. Furthermore, the iterative updates in the ADMM maintain information exchanges between the energy minimizations for SAR image deblurring and oil spill segmentation. Most existing blurry image segmentation strategies tend to consider deblurring and segmentation as two independent procedures with no interactions, and the operation of deblurring is thus not guided for obtaining an accurate segmentation. In contrast, we integrate deblurring and segmentation into one overall energy minimization framework with information exchanges between the two procedures. Therefore, the deblurring procedure is inclined to operate in favor of the more accurate oil spill segmentation. Experimental evaluations validate that our framework outperforms the separate deblurring and segmentation strategy for detecting oil spill regions in blurry SAR images.
Highlights
M ARINE oil spill accidents, which have caused various damages to the natural environment, have frequently occurred at different scales [1]
The non-Bragg scattering phenomenon caused by oil spills is a major physical feature for oil spill analysis based on synthetic aperture radar (SAR) images
The capillary and short gravity waves on the ocean surface give rise to Bragg scattering that is sensed by SAR, and marine oil spills result in dark patches in SAR images by damping out the Bragg scattering
Summary
M ARINE oil spill accidents, which have caused various damages to the natural environment, have frequently occurred at different scales [1]. Researchers mainly from the image processing community have been working on developing more sophisticated oil spill segmentation methods for accurately detecting oil spill regions in SAR images. In this scenario, one common formulation of oil spill segmentation is energy minimization, where an energy functional measures the segmentation characteristics such as fitness and similarity with respect to the oil spills. It maintains information conveyed between the subproblems such that the subproblem solutions are compatible with one another and achieve the final solution of the overall energy minimization We exploit this advantage of ADMM for conducting deblurring which provides helpful information for oil spill segmentation, and establish a framework that simultaneously deblurs and segments oil spill. Experimental evaluation validates the effectiveness of our method for segmenting blurry oil spill SAR images
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