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Abstract
In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic aperture radar (SAR) images. In the first stage, we propose a new variational model to reduce speckles in non-textured SAR images. Applications to simulated and real SAR images show that the method is well balanced in the quality of the conventional criteria. Then, in the second stage, we use the fast Chan–Vese (CV) model and the level set method to segment the oil slick in the de-speckled SAR image. The additive operator splitting (AOS) scheme is used in the numerical implementation to improve computational efficiency. Experimental results show that our two-stage algorithm is effective for oil slick segmentation in SAR images.
Acknowledgements
This work is partially supported by the National Science Foundation of China (60773119, 10971066), the National Science Foundation of Shanghai (10ZR1410200) and the Research Fund for the Doctoral Program of Higher Education (200802691037). The authors are grateful to the anonymous referees for helpful suggestions.