Segmenting Oil Spills from Blurry Images Based on Alternating Direction Method of Multipliers

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.