Coastline Detection Based on Sentinel-1 Time Series for Ship- and Flood-Monitoring Applications

Abstract

This letter addresses the use of the Sentinel-1 time series with the aim of proposing an automatic and unsupervised coastline detection method that averages the dynamical variations of coastal areas over a limited period of time, e.g., one year. First, we propose applying a temporal averaging filter that allows the temporal variations in coastal areas, e.g., due to tides or vegetation, to be encapsulated, and, at the same time, the speckle to be reduced, without decreasing the spatial resolution of the synthetic aperture radar (SAR) time series. Then, based on the distinctive backscattering values of the sea and land pixels, we will employ an iterative hierarchical tiling method in order to accurately characterize the two classes using bimodal distribution. The distribution is then segmented by a thresholding and regiongrowing procedure to separate the sea and land classes. A largescale quantitative comparison between the SAR-derived and open street map (OSM) coastlines allows for a numerical evaluation of the results, i.e., an overall agreement ranging from 80% to 90%. In addition, Sentinel-2 images are used to evaluate the estimated SAR coastline qualitatively. Furthermore, the benefits of having an accurate SAR coastline are shown in the case of two well-known Earth observation-monitoring applications, ship detection, and floodwater mapping.

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

“…As shown in Figure 10a, the morphology of the area may represent a limiting factor of the methodology. However, imagery collected using a different polarization combination can help overcome this issue (see Figure 10b) allowing a more accurate water/land classification [83].…”

Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

“…It is expensive, is susceptible to weather, and has a limited working distance. In view of the many disadvantages of radar images, optical remote sensing images, such as Sentinel [21,22], GeoEye [23], IKONOS [24], Pleiades [25], Spot [26], RapidEye [27], Landsat [9,[28][29][30][31], and WorldView-2 [7,32], have been used by many scholars in coastline research and analysis. Different types of optical images, however, have distinct advantages and different application ranges.…”

Spatiotemporal Changes of Coastline over the Yellow River Delta in the Previous 40 Years with Optical and SAR Remote Sensing

“…This greatly impedes available high-quality optical and multispectral observations and further affects the extraction of dynamic coastline information. As a microwave remote sensing technology, SAR can penetrate clouds and provide an important complementary data acquisition, which has been successfully used to track coastlines, coastal erosion, and accretion patterns, in addition to various geological hazards [1,32,[34][35][36].…”