Advanced Lake Shoreline Extraction Approach by Integration of SAR Image and LIDAR Data

Abstract

Noise and an abnormal distributed-image histogram is the main challenge of using SAR data. From this point of view, this study’s authors motivated the non-use of user-defined input parameters. To achieve this purpose, a fuzzy approach was proposed to extract shoreline from SENTINEL-1A data. The parameters in the processing of the SENTINEL-1A image were generated automatically with LIDAR-intensity-derived object-based segmentation results. The LIDAR-intensity image was segmented with the Mean-shift method. The corresponding result was used to estimate the input parameters for fuzzy clustering of the SENTINEL-1A image. Fuzzy segmentation was proposed, due to the expected large number of values regarding water and land classes except for the pixels along the shoreline. The memberships for land and water classes were separately computed. In the proposed approach, the results from LIDAR and SENTINEL-1A dataset are promising, with differences below 1 pixel (10 m) by evaluation with the used reference vector data.

Keywords:

• Data fusion
• remote sensing
• SAR
• shoreline detection

Additional information

Funding

This study has been supported by TUBITAK (The Scientific and Technological Research Council of Turkey), with project number 115Y718. Authors also thank to the HGM (Turkish General Directorate of Mapping) for providing the LIDAR dataset used in this study.