FN Archimer Export Format
PT J
TI Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method
BT 
AF Minghelli, Audrey
   Spagnoli, Jérôme
   Lei, Manchun
   Chami, Malik
   Charmasson, Sabine
AS 1:1;2:1;3:2;4:3;5:4;
FF 1:;2:;3:;4:;5:;
C1 Université de Toulon, SeaTech, CNRS, LIS Laboratory UMR 7020, 83041 Toulon, France
   Université Paris-Est, LaSTIG, IGN, ENSG, 94160 Saint-Mandé, France
   Sorbonne Université, CNRS-INSU, LATMOS, CEDEX, 06304 Nice, France
   Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Centre Ifremer, 83507 La Seyne sur Mer, France
C2 UNIV TOULON, FRANCE
   UNIV PARIS EST, FRANCE
   CNRS, FRANCE
   IRSN, FRANCE
IN DOAJ
IF 2.1
TC 10
UR https://archimer.ifremer.fr/doc/00649/76116/77079.pdf
LA English
DT Article
DE ;shoreline;foam;classification;WorldView-2;multispectral;high resolution satellite images
AB 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 
PY 2020
PD AUG
SO Remote Sensing
SN 2072-4292
PU MDPI AG
VL 12
IS 16
UT 000565447000001
DI 10.3390/rs12162664
ID 76116
ER

EF