High-resolution bathymetry surveys provide an opportunity to analyse local geological structure where onshore areas afford limited exposure. Semi-automated lineament detection methods are necessary for areas of large coverage where a manual analysis would be subjective and time-consuming. However, semi-automated approaches are dependent on effective feature extraction methods to identify genuine lineaments. This study offers solutions to common problems that can impede processing methods where sharp steps in the seafloor (e.g. palaeocoastlines) are present. Directional gradient, Sobel and Laplacian filters are explored as well as the hillshade and tilt derivative transform for feature extraction prior to applying an object-based image analysis lineament detection approach. The filtered datasets generally perform poorly with a marked improvement when using the hillshade transform. However, we find the azimuth-invariant tilt derivative, which incorporates a convolved vertical derivative, to be the most successful, identifying lineaments in a range of orientations and across a sharp step in the seafloor.

高分辨率测深调查提供了一个机会，可以分析在沿海地区接触有限的地方的地质结构。对于覆盖范围大的区域，半自动的线条检测方法是必要的，在这些区域中，手动分析将是主观且耗时的。但是，半自动方法依赖于有效的特征提取方法来识别真正的线条。这项研究提供了解决常见问题的解决方案，这些问题可能会阻碍存在海底陡峭台阶（例如古海岸线）的加工方法。在应用基于对象的图像分析线条检测方法之前，先探索了方向梯度，Sobel和Laplacian滤波器以及山体阴影和倾斜导数变换以进行特征提取。使用hillshade变换时，过滤后的数据集通常表现不佳，但有明显的改进。但是，我们发现结合卷积垂直导数的方位不变倾角导数是最成功的方法，它可以识别出一系列方向和海底中一个陡峭台阶上的直线。