Shoreline extraction using unsupervised classification on Sentinel-2 imagery

Shoreline extraction is a key process for many coastal zone applications, such as navigation and coastal environmental protection. The manual extraction of shorelines manually is tedious and subject to the operator’s ability. The main objective of this research is to evaluate the use of two different image fusion techniques (IHS and PCA - Principal Component Analisys) using near-infrared band on multispectral Sentinel-2 imagery to extract shoreline in the coastal zone of Cassino beach, Southern Brazil. The resulting images were classified into two classes (water and non-water) using the K-Means algorithm, and the accuracy was evaluated through the analysis of mean absolute difference and RMSE applied on segments of artificial coastal structures. The results indicate that the shoreline extraction by PCA method obtained the most accurate results, and the use of sharpened MNDWI (Modified Normalized Difference Water Index) image shows a good alternative to improve shoreline extraction.