由于SAR图像固有的相干斑噪声和海陆交界处复杂的地形影响, 利用区域合并方法在进行SAR图像的海岸线提取过程中很容易出现误合并。为解决SAR图像海岸线分割中单一分割尺度造成的误分割问题, 提出基于SLIC超像素的SAR图像海岸线分割算法, 超像素分割后再利用改进的融合光谱和纹理信息的合并代价(CT-Model)进行合并, 最后将海陆交界处的海岸线显示用来进行分割效果对比。实验结果表明, 改进后的合并准则在SAR图像的海岸线分割上具有更好的精确度。

Due to the inherent speckle noise of SAR image and the influence of complicated terrain at the sea-land boundary, it is easy to incorrectly extract the coastline of SAR image by using the region merging method. In order to solve the problem of incorrect segmentation caused by a single scale, an algorithm for coastline segmentation of SAR image based on SLIC super-pixel is proposed.Super-pixel segmentation is used to preprocess the image at first.Then, the improved merging cost based on spectral and texture information (CT-Model) is used to merge, and finally the segmentation results are obtained. The coastline at the sea-land boundary is used for the comparison of segmentation effects. The experimental results show that the improved merging method is more accurate in coastline segmentation than the spectral-histogram method.