Unsupervised classification based approach for coastline extraction from Sentinel-2 imagery

Abstract

Coastline extraction techniques from multispectral satellite images are of great interest for protection and monitoring of coastal areas. In this regard, the Sentinel-2 satellites can give a great contribution thanks to their wide coverage of the earth's surface. These images can be processed by GIS software, so as to detect the sea from all the rest. However, the traditional supervised classification requires the involvement of the operator to create suitable training sites: this approach, in addition to being associated to the operator's skill, often takes a long time to be completed. This contribution presents a study carried out on Sentinel-2 dataset and proposes the application of an unsupervised classification method, the k-means, on four different classification indices. The coastlines extracted by unsupervised classification are therefore compared with the coastline manually vectorized from the RGB composition. The results demonstrate the effectiveness of k-means for distinguishing, in the images produced by the indices application, two clusters (water / no-water) in a reduced time lapse if compared with the traditional supervised techniques.

High-Resolution Real-Time Coastline Detection Using GNSS RTK, Optical, and Thermal SfM Photogrammetric Data in the Po River Delta, Italy

“…The extraction of coastline from optical images can be performed automatically by means of various methods developed in recent decades. Supervised [15] and unsupervised [16,17] image classification methods, specific tools (Automatic Coastal Extraction Tool [18]) and suitable indices [19] can be used. In the latter approach, many indices are available to detect the coastline in an easy way using satellite images [20][21][22][23][24][25].…”

A Shoreline Extraction Method Based on Double-Loop Network Framework

“…Sun et al [24] proposed a hyperpixel-based conditional random field model that segments the ocean and land to extract coastlines. Alcaras et al [25] employed K-means clustering to divide remote sensing images into two classes, ocean and land, facilitating the extraction of coastlines. Dewi et al [26] introduced a fuzzy c-means method to address the uncertainty in coastline changes, resulting in more accurate coastline extraction.…”

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

“…Trained human interpreters combine spectral information viewed from the image with contextual information concerning the nature of the study environment to identify, delineate and classify specific features such as land cover, land use and, if the resolution permits, specific objects [12]. In consequence, the knowledge given by the expert on the different thematic object classes present in the image supports interpretation of coastal areas [13] and consequently provides information for coastline visual detection and manual vectorization [14,15].…”

“…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].…”