Abstract
Inshore ship detection is an important research direction of synthetic aperture radar (SAR) images. Due to the effects of speckle noise, land clutters and low signal-to-noise ratio, it is still challenging to achieve effective detection of inshore ships. To solve these issues, an inshore ship detection method based on the level set method and visual saliency is proposed in this paper. First, the image is fast initialized through down-sampling. Second, saliency map is calculated by improved local contrast measure (ILCM). Third, an improved level set method based on saliency map is proposed. The saliency map has a higher signal-to-noise ratio and the local level set method can effectively segment images with intensity inhomogeneity. In this way, the improved level set method has a better segmentation result. Then, candidate targets are obtained after the adaptive threshold. Finally, discrimination is employed to get the final result of ship targets. The experiments on a number of SAR images demonstrate that the proposed method can detect ship targets with reasonable accuracy and integrity.
Highlights
Ship detection for synthetic aperture radar (SAR) images is of great importance in both military and commercial applications [1,2,3]
Derived from Local Binary Fitting (LBF) model and improved local contrast measure (ILCM), an inshore ship detection method based on the level set method and visual saliency is proposed in this paper
This paper mainly focuses on inshore ship detection, so we only consider a two-phase level set formulation
Summary
Ship detection for synthetic aperture radar (SAR) images is of great importance in both military and commercial applications [1,2,3]. The level set method (LSM) has been widely applied in the fields of image processing [8] and computer vision. This method uses the geometric metrics of the curve such as the curvature and the normal vector to control the movement of the curve, so it does not depend on the parameters of the curve and can handle the changes of the topology.
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