Accuracy Evaluation of Coastline Extraction Methods in Remote Sensing: A Smart Procedure for Sentinel-2 Images

Abstract

Abstract. Different algorithms are available in literature to extract coastline from remotely sensed images and different approaches can be adopted to evaluate the result accuracy. In every case, a reference coastline is suitable to compare alternative solutions: usually, the visual photointerpretation on the RGB composition of the considered imagery and the manually vectorization of the coastline allow an accurate term of comparison, but they are laborious and time consuming. This article aims to demonstrate that a smart procedure is possible using a LiDAR-generated Digital Elevation Model (Lg-DEM) as a useful source from which to rapidly extract the reference coastline. The experiments are carried out on Sentinel-2 imagery, using six indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Enhanced Vegetation Index (EVI), Red-Green Ratio (RGR) and NIR-Red Ratio (NRR). The unsupervised classification algorithm named K-Means transforms each index resulting product in two clusters, i.e. water and no-water, while the automatic vectorization allows to detect the coastline as separation between land and sea. The coastline from Lg-DEM and the manually achieved one using photointerpretation are both assumed as references for testing result accuracy. In every case, the performance analysis of the six indices products induces similar results, confirming the combination of NDWI and K-Means as the most performing approach. The tests demonstrate that, when Lg-DEM and satellite images concern the same area in the same period or in absence of variations, the coastline extracted from Lg-DEM is useful as reference to compare various methods.

Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach

“…The second index adopts the same type of formula as NDWI but uses a different combination of bands in order to improve the identification of water bodies, for example, in urban areas or in the presence of eutrophication phenomena. According to [75,76], in this study we apply MNDWI by the following formulas related to the used dataset:…”

Modeling and Accuracy Assessment of Determining the Coastline Course Using Geodetic, Photogrammetric and Satellite Measurement Methods: Case Study in Gdynia Beach in Poland

“…The most used definitions of coastline based on the time scale are instantaneous coastline, short-term coastline, and long-term coastline [4]. The instantaneous coastline is defined as the position of the land/sea separation line in an instant of time [5]. The short-term coastline requires multiple locations of the line under consideration at the rate of several samples at a time, for a limited period [6].…”

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

“…Nevertheless, supervised classification techniques are largely used for coastline extraction, especially when accurate results are required, such as for high and very high-resolution images [17]. Rather than on the satellite images, unsupervised techniques are more frequently applied to the products of their processing based on other algorithms [15,18].…”