Shoreline position is essential information for autonomous vehicle navigation, river management such as monitoring channel flow changes, pollution tracking, and characterization of riverine currents and erosion processes. Waterway surveys taken solely from the land can be prohibitive due to challenging terrain or outright inaccessibility of a region. Often such surveys desire such high levels of spatial resolution that they become economically infeasible. Modern advances in the availability, quality, resolution, and processing methodologies for high resolution imagery render it an attractive, alternative source for shoreline information. While edges in imagery data are easily detected by the human eye, significant efforts in recent times have been devoted to the automated extraction of these edges, though each of these approaches has drawbacks and shortfalls.
The problem of extracting accurate shoreline information from remotely sensed images such as imagery from Synthetic Aperture Radar (SAR) or digital imagery obtained from satellites or aircraft is an on-going concern facing the remote sensing and geographical modeling communities. This is a very important problem since knowledge of a shoreline position over large areas is essential for autonomous ship navigation, coastal management, and coastal modeling of currents and erosion. In addition, it is often difficult to survey a region solely from the land since terrain and/or accessibility can often prohibit data collection in some coastal areas, necessitating the use of remote sensing methods. However, manually determining shorelines from aerial photographs is a time-consuming process that is prone to errors due to the inherent subjectivity of an individual cartographer. Thus, automated software-based methods are currently a topic of significant interest.
The standard approaches for solving the shoreline extraction problem have involved first locating the land-water boundary through edge detection and then delineating the location of the boundary through line segment tracing. The practical use of edge data, for numerical modeling or geographical information systems (GIS) purposes, requires the edge coordinates be ordered in either a clockwise or counterclockwise manner and be self-consistent.
There are a number of well-known algorithms for edge detection and boundary tracing. For example, to determine the “edge” region between land and water, the variation (or gradient) in pixel intensity is typically used. However, because pixilated images have a discrete resolution and the contrast between the land and the water can be poor, sophisticated methods have been developed for isolating the edge pixels. See J. S. Lee, et al., “Coastline detection and tracing in SAR images.” IEEE Transactions on Geoscience and Remote Sensing, 28(4), 662-668 (1990); and Y. Yu, “Automated delineation of coastline from polarimetric SAR imagery,” International Journal of Remote Sensing, 25 (17), 3423-3438 (2004). This becomes a particularly difficult process in either extremely shallow water with sandy bottoms or when there are shadows on the water caused by vegetation around the water edge. In these cases small differences between radiance values of the land and water can exist at many wavelengths commonly used in edge discrimination.
The boundary tracing procedure is also quite complex and requires a number of steps to determine the line segments that accurately represent the shoreline. Because the boundary tracing process can lead to disconnected line segments if the boundary is not well defined due to poor edge resolution, a number of methods for connecting boundary segments have been developed as well. See G. Gorman, et al., “Shoreline approximation for unstructured mesh generation,” Computers and Geosciences, 33, 666-667 (2007); and I. Kolingerova, et al., “Reconstructing boundaries within a given set of points, using Delaunay triangulation,” Computers and Geosciences, 32, 1310-1319 (2006).
In addition, small regions of isolated “land” or “water” points can also be eliminated from the shoreline boundary based on some reasonable criteria to distinguish significant land or water masses from noise in the images. An essential part of tracing the boundary involves determining how to connect neighboring edge pixels to each other in such a manner that a smooth boundary is produced. The most widely used method to accomplish this involves an eight point “stencil” centered at each edge pixel. The eight nearest neighbors of a pixel box (four face sharing boxes and four corner touching boxes along the diagonals of the central pixel box) are searched in order to determine the nearest neighboring “edge” pixel that minimizes the angular change from the existing boundary line segment. Using this approach an initial list of line segment vectors can be created. After this initial step, the boundary can be further refined by joining larger multi-vector segments.
A drawback of this approach is that it must be assumed that the coastline does not change rapidly in the tracing direction (i.e. the minimum angle constraint) and a searching procedure must be implemented to determine the nearest pixel (of the eight neighbors) that satisfies the minimum angle criteria. Also, this approach does not enforce any particular tracing direction (clockwise or counter-clockwise) around the land-water boundary. As a result, there is an additional post-processing step required to create a vectorized shoreline data set, which requires additional time and processing resources.
Other approaches for extracting shorelines can involve more complex transformations of the remotely sensed imagery (i.e. Watershed transform, Hough transform, active contouring or “snakes”, classification schemes). See D. C. Mason, et al., “Accurate and efficient determination of the shoreline in ERS-1 SAR images,” IEEE Transactions on Geoscience and Remote Sensing, 34(5), 1243-1253 (1996). The primary drawback of these methods is that they involve a number of mathematical procedures that all have various caveats with regard to how they may be applied. In addition, although these approaches are mathematically rigorous they may not be robust in their applicability to all intended problems and thus may require additional refinements and checking procedures to ensure the quality of the extracted shoreline.