Abstract
Shoreline mapping using satellite remote sensing images has the advantages of large-scale surveys and high efficiency. However, low spatial resolution, various geometric morphologies and complex offshore environments prevent accurate positioning of the shoreline. This article proposes a semi-global subpixel shoreline localization method that considers utilizing morphological control points to divide the initial artificial shoreline into segments of relatively simple morphology and analyzing the local intensity homogeneity to calculate the intensity integral error. Combined with the segmentation-merge-fitting method, the algorithm determines the subpixel location accurately. In experiments, we select five artificial shorelines with various geometric morphologies from Landsat 8 Operational Land Imager (OLI) data. The five subpixel artificial shoreline RMSE results lie in the range of 3.02 m to 4.77 m, with line matching results varying from 2.51 m to 3.72 m. Thus, it can be concluded that the proposed subpixel localization algorithm is effective and applicable to artificial shoreline in various geometric morphologies and is robust to complex offshore environments, to some extent.
Highlights
The coastline, the boundary of land and sea, is one of the 27 most important land surface features, and is vulnerable to natural processes such as coastal erosion/accretion, sea level changes and human activities [1]
With the merits of efficient, large-scale investigational capability, satellite remote sensing shoreline mapping plays an important role in the monitoring of coastal resource management
Low spatial resolution, various shoreline geometric morphologies, and complex offshore environments prevent the accurate positioning of shorelines
Summary
The coastline, the boundary of land and sea, is one of the 27 most important land surface features, and is vulnerable to natural processes such as coastal erosion/accretion, sea level changes and human activities [1]. With the advantages of cost-effectiveness and large spatial and temporal scales, satellite remote sensing data have been used widely for coastline mapping [1,7,8,9]. Most observed shoreline changes are presently much smaller [12], so that the coarse spatial resolution of pixels prevent the accurate determination of shoreline positions when monitoring shoreline changes [13]. In this case, shoreline change observations can only be obtained by means of repeated in situ surveys, analysis of aerial or satellite high-resolution photographs at several time intervals, or a combination of both approaches [11,14]. It is important to conduct research on how to accurately determine the shoreline’s position from long-term sequences of medium spatial resolution satellite images
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