Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method

Type Article
Date 2020-08
Language English
Author(s) Minghelli Audrey1, Spagnoli Jérôme1, Lei Manchun2, Chami Malik3, Charmasson Sabine4
Affiliation(s) 1 : Université de Toulon, SeaTech, CNRS, LIS Laboratory UMR 7020, 83041 Toulon, France
2 : Université Paris-Est, LaSTIG, IGN, ENSG, 94160 Saint-Mandé, France
3 : Sorbonne Université, CNRS-INSU, LATMOS, CEDEX, 06304 Nice, France
4 : Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Centre Ifremer, 83507 La Seyne sur Mer, France
Source Remote Sensing (2072-4292) (MDPI AG), 2020-08 , Vol. 12 , N. 16 , P. 2664 (17p.)
DOI 10.3390/rs12162664
WOS© Times Cited 1
Keyword(s) shoreline, foam, classification, WorldView-2, multispectral, high resolution satellite images
Abstract

Foam is often present in satellite images of coastal areas and can lead to serious errors in the detection of shorelines especially when processing high spatial resolution images (<20 m). This study focuses on shoreline extraction and shoreline evolution using high spatial resolution satellite images in the presence of foam. A multispectral supervised classification technique is selected, namely the Support Vector Machine (SVM) and applied with three classes which are land, foam and water. The merging of water and foam classes followed by a segmentation procedure enables the separation of land and ocean pixels. The performance of the method is evaluated using a validation dataset acquired on two study areas (south and north of the bay of Sendaï—Japan). On each site, WorldView-2 multispectral images (eight bands, 2 m resolution) were acquired before and after the Fukushima tsunami generated by the Tohoku earthquake in 2011. The consideration of the foam class enables the false negative error to be reduced by a factor of three. The SVM method is also compared with four other classification methods, namely Euclidian Distance, Spectral Angle Mapper, Maximum Likelihood, and Neuronal Network. The SVM method appears to be the most efficient to determine the erosion and the accretion resulting from the tsunami, which are societal issues for littoral management purposes

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How to cite 

Minghelli Audrey, Spagnoli Jérôme, Lei Manchun, Chami Malik, Charmasson Sabine (2020). Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method. Remote Sensing, 12(16), 2664 (17p.). Publisher's official version : https://doi.org/10.3390/rs12162664 , Open Access version : https://archimer.ifremer.fr/doc/00649/76116/