A Variational Level Set Remote Sensing SAR Image Segmentation Approach for Oil Spill Detecting Based on Fuzzy Cluster

Abstract:

Synthetic Aperture Radar (SAR) has become one of the important means for the ocean remote sensing detection of oil spills. The existing SAR image segmentation method has the issues of edge blur, poor contrast, non-uniform intensity image, so the segmentation effect is not ideal. This paper presents a variational level set SAR image of oil spill detection method based on fuzzy clustering. First of all, apply the threshold method on initial segmentation of the original SAR image to transform the initial segmented image as fuzzy clustering. Secondly, introduce the clustering results into the initial level set function to achieve the initial contour. Finally, add fuzzy clustering model in the level set energy function to complete the level set evolution process and get the final segmented image. This paper uses the threshold segmentation results to achieve the initialization of the variational level set function profile. In theory, it could improve the level set method for efficiency, and reduce the wrong segmentation phenomenon. The experimental results show that the SAR image segmentation method of oil spill has good segmentation qualities and is suitable for the edge complex image segmentation.

• Periodical: Applied Mechanics and Materials (Volumes 719-720)
• Pages: 1049-1055
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.719-720.1049
• Online since: January 2015
• Authors: Jin Yu Liu*, Zheng Ning Zhang, He Meng Yang
• Keywords: Fuzzy Cluster, Image Segmentation, Level Set, Oil Spill Detection, SAR Image

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