Abstract Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study’s authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10 m) by evaluation with the used reference vector data.

中文翻译：

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结合 SAR 图像和 LIDAR 数据的高级湖岸线提取方法

摘要 噪声和异常分布的图像直方图是使用 SAR 数据的主要挑战。从这个角度来看，这项研究的作者促使不使用用户定义的输入参数。为了达到这个目的，提出了一种模糊方法来从 SENTINEL-1A 数据中提取海岸线。SENTINEL-1A 图像处理中的参数是使用 LIDAR 强度衍生的基于对象的分割结果自动生成的。激光雷达强度​​图像用Mean-shift 方法分割。相应的结果用于估计 SENTINEL-1A 图像模糊聚类的输入参数。提出了模糊分割，因为除了沿海岸线的像素外，预计会有大量关于水和土地类别的值。土地和水类的成员是分开计算的。