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Multi scale C-V model level set method for fast coastline extraction with SAR imagery

机译:利用SAR图像快速提取海岸线的多尺度C-V模型水平集方法

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This paper proposes a fast, high-precision of multi-scale C-V model (M-C-V) method for coastline extraction with SAR imagery. (1) The multi-scale technology to the traditional C-V model is introduced that reduces the image size and obtains a series of images under different spatial resolution; (2) Low-pass filter polishes small scale image sequence is used, which is easy to form the image sequence with the relatively smooth boundary; (3) The image sequence in different scales and polished degree, splits the coastline based on the C-V model of level set one by one. In the division of coastline, high spatial resolution image inherits the boundary which is extracted by low spatial resolution images in the higher level and refines coastline further through the C-V model. Experiments show that the method accelerates the acquisition of initial level set formation, shortens the time of the extraction of coastline, at the same time, removes the non-coastline body part and improves the identification precision of the main body coastline, which makes the extracted process of coastline robust.
机译:提出了一种快速,高精度的多尺度C-V模型(M-C-V)方法,用于SAR图像的海岸线提取。 (1)引入了传统C-V模型的多尺度技术,以减小图像尺寸并获得不同空间分辨率下的一系列图像; (2)采用低通滤波器对小规模的图像序列进行抛光,易于形成边界相对平滑的图像序列; (3)不同尺度和抛光度的图像序列,基于水平设置的C-V模型一一划分海岸线。在海岸线划分中,高空间分辨率图像继承了边界,该边界由较高级别的低空间分辨率图像提取,并且通过C-V模型进一步细化了海岸线。实验表明,该方法加快了初始水平集形成的获取,缩短了海岸线提取的时间,同时,去除了非海岸线的身体部位,提高了主体海岸线的识别精度,使得提取的主体海岸线更为合理。海岸线稳健的过程。

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