Volume IV-2/W4
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 445-449, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-445-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 445-449, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W4-445-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  14 Sep 2017

14 Sep 2017

INTEGRATED SHORELINE EXTRACTION APPROACH WITH USE OF RASAT MS AND SENTINEL-1A SAR IMAGES

N. Demir1, S. Oy1, F. Erdem2, D. Z. Şeker3, and B. Bayram2 N. Demir et al.
  • 1Akdeniz University, Space Science and Technologies, Antalya, Turkey
  • 2Yildiz Technical University, Dept. of Geomatic Engineering, Istanbul, Turkey
  • 3Istanbul Technical University, Department of Geomatics Engineering, 34469, Maslak Istanbul Turkey

Keywords: SAR, Shoreline, Multispectral image, RASAT, Fuzzy clustering

Abstract. Shorelines are complex ecosystems and highly important socio-economic environments. They may change rapidly due to both natural and human-induced effects. Determination of movements along the shoreline and monitoring of the changes are essential for coastline management, modeling of sediment transportation and decision support systems. Remote sensing provides an opportunity to obtain rapid, up-to-date and reliable information for monitoring of shoreline. In this study, approximately 120 km of Antalya-Kemer shoreline which is under the threat of erosion, deposition, increasing of inhabitants and urbanization and touristic hotels, has been selected as the study area. In the study, RASAT pansharpened and SENTINEL-1A SAR images have been used to implement proposed shoreline extraction methods. The main motivation of this study is to combine the land/water body segmentation results of both RASAT MS and SENTINEL-1A SAR images to improve the quality of the results. The initial land/water body segmentation has been obtained using RASAT image by means of Random Forest classification method. This result has been used as training data set to define fuzzy parameters for shoreline extraction from SENTINEL-1A SAR image. Obtained results have been compared with the manually digitized shoreline. The accuracy assessment has been performed by calculating perpendicular distances between reference data and extracted shoreline by proposed method. As a result, the mean difference has been calculated around 1 pixel.