Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1565-1570, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1565-2018
© 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, 1565-1570, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-1565-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

SUBTYPE COASTLINE DETERMINATION IN URBAN COAST BASED ON MULTISCALE FEATURES: A CASE STUDY IN TIANJIN, CHINA

Y. Song1, Y. Ai1, and H. Zhu2,3 Y. Song et al.
  • 1China University of Geosciences, Wuhan, China
  • 2National Satellite Ocean Application Service, Beijing 100081, China
  • 3Key Laboratory of Technology for Safeguarding of Maritime Rights and Interests and Application, State Oceanic Administration, Guangzhou 510310, China

Keywords: Subtype Coastline, Multiscale Feature, Uncertainty, Remote Sensing, Tianjin

Abstract. In urban coast, coastline is a direct factor to reflect human activities. It is of crucial importance to the understanding of urban growth, resource development and ecological environment. Due to complexity and uncertainty in this type of coast, it is difficult to detect accurate coastline position and determine the subtypes of the coastline. In this paper, we present a multiscale feature-based subtype coastline determination (MFBSCD) method to extract coastline and determine the subtypes. In this method, uncertainty-considering coastline detection (UCCD) method is proposed to separate water and land for more accurate coastline position. The MFBSCD method can well integrate scale-invariant features of coastline in geometry and spatial structure to determine coastline in subtype scale, and can make subtypes verify with each other during processing to ensure the accuracy of final results. It was applied to Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images of Tianjin, China, and the accuracy of the extracted coastlines was assessed with the manually delineated coastline. The mean ME (misclassification error) and mean LM (Line Matching) are 0.0012 and 24.54 m respectively. The method provides an inexpensive and automated means of coastline mapping with subtype scale in coastal city sectors with intense human interference, which can be significant for coast resource management and evaluation of urban development.