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Level sets with self-guided filtering for marine oil spill segmentation

Segmentation of marine oil spill regions from SAR images has always been an important research topic in the remote sensing literature. One challenge for this problem is how to precisely preserve oil spill edges in segmentation results. To address this challenge, we present an edge sensitive algorithm for marine oil spill segmentation based on an energy minimization formulation. Specifically, we use an edge indicator in penalty terms of the energy function, for the purpose of detecting possible oil spill edges. Furthermore, a self-guided filtering scheme is incorporated into the energy function, which is capable of smoothing images without blurring edges. The energy minimization is conducted based on level set evolution, in which a double well distance regularization is involved for avoiding evolutionary irregularities. Empirical evaluations reveal that our method outperforms state of the art edge-based level set methods in marine oil spill segmentation.