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In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications. [ABSTRACT FROM AUTHOR]

Accurate coastline position is of fundamental importance for many applications concerning coastal zone monitoring, management, and planning. For example, coastal erosion phenomena require a careful and continuous monitoring due to the dynamic nature of the coastline which can undergo sudden and significant changes in position and shape over time. Various techniques allow acquiring the coastline, among these the use of multispectral optical sensors operating from satellites is one of the most widespread. With the advent of high and very high geometric resolution (VHR) satellites, it is possible to obtain images with a pixel size of less than 1 m that allow extracting accurate coastlines. The purpose of this article is to define a fast approach to investigate the degree of accuracy of one of the most popular techniques for the automatic extraction of the coastline, based on the Normalized Difference Water Index (NDWI) use. In this study, the coastline is achieved from VHR Pléiades imagery (2 m for multispectral and 0.5 m for panchromatic). Therefore, NDWI is obtained and processed both from initial images and pan-sharpened images. The resulting coastlines are submitted to smoothing and their accuracy is therefore evaluated. For this purpose, a reference coastline is manually achieved from panchromatic image. Two different methods are proposed for coastline accuracy evaluation, both based on the geometrical analysis of the polygons generated by the intersection between the extracted coastline and the reference one. This study demonstrates that the proposed methods permit to easily evaluate the accuracy of the extracted coastline; in addition, the results confirm the effectiveness of NDWI and highlight the limited benefits of pan-sharpened images for this index application. [ABSTRACT FROM AUTHOR]