Volume XLII-3/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 513-518, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-513-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 513-518, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-W4-513-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Mar 2018

06 Mar 2018

COASTLINE DETECTION USING FUSION OF OVER SEGMENTATION AND DISTANCE REGULARIZATION LEVEL SET EVOLUTION

S. Toure1, O. Diop1, K. Kpalma2, and A. S. Maiga1 S. Toure et al.
  • 1Laboratoire d’Electronique Informatique Telecommunications et Energies Renouvelables Universit Gaston Berger de Saint-Louis, Senegal
  • 2Institut d’Electronique et Telecommunications de Rennes /INSA Rennes, France

Keywords: Coastline, DRLSE, FOOS, K-means Clustering, Sea-land Segmentation, Wavelet Transform

Abstract. Coastline detection is a very challenging task in optical remote sensing. However the majority of commonly used methods have been developed for low to medium resolution without specification of the key indicator that is used. In this paper, we propose a new approach for very high resolution images using a specific indicator. First, a pre-processing step is carried out to convert images into the optimal colour space (HSV). Then, wavelet decomposition is used to extract different colour and texture features. These colour and texture features are then used for Fusion of Over Segmentation (FOOS) based clustering to have the distinctive natural classes of the littoral. Among these classes are waves, dry sand, wet sand, sea and land. We choose the mean level of high tide water, the interface between dry sand and wet sand, as a coastline indicator. To find this limit, we use a Distance Regularization Level Set Evolution (DRLSE), which automatically evolves towards the desired sea-land border. The result obtained is then compared with a ground truth. Experimental results prove that the proposed method is an efficient coastline detection process in terms of quantitative and visual performances.