A GΓD based level set method for target segmentation in high resolution SAR images

2017 ◽  
Author(s):  
Bin Wang ◽  
Guoyu Wang
Keyword(s):  
High Resolution ◽  
Level Set Method ◽  
Level Set ◽  
Sar Images ◽  
Target Segmentation

A level set method for oil slick segmentation in SAR images

2005 ◽  
Vol 26 (6) ◽  
pp. 1145-1156 ◽  
Author(s):  
B. Huang ◽  
H. Li ◽  
X. Huang
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Sar Images ◽  
Oil Slick

scholarly journals Inshore Ship Detection Based on Level Set Method and Visual Saliency for SAR Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3877 ◽  
Author(s):  
Tao Xie ◽  
Weike Zhang ◽  
Linna Yang ◽  
Qingping Wang ◽  
Jingjian Huang ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Signal To Noise Ratio ◽  
Visual Saliency ◽  
Saliency Map ◽  
Local Contrast ◽  
Signal To Noise ◽  
Sar Images ◽  
Ship Detection ◽  
Noise Ratio

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.

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