Effect of different segmentation methods using optical satellite imagery to estimate fuzzy clustering parameters for Sentinel-1A SAR images

Optical and SAR data are efficient data sources for shoreline monitoring. The processing of SAR data such as feature extraction is not an easy task since the images have totally different structure than optical imagery. Determination of threshold value is a challenging task for SAR data. In this study, SENTINEL-2A optical data was used as ancillary data to predict fuzzy membership parameters for segmentation of SENTINEL-1A SAR data to extract shoreline. SENTINEL-2A and SENTINEL-1A satellite images used were taken in September 9, 2016 and September 13, 2016 respectively. Three different segmentation algorithms which are selected from object, learning and pixel-based methods. They have been exploited to obtain land and water classes which have been used as an input data for parameter estimation. Thus, the performance of different segmentation algorithm has been investigated and analysed. In the first step of the study, Mean-Shift, Random Forest and Whale Optimization algorithms have been employed to obtain water and land classes from the SENTINEL-2A image. Water and land classes derived from each algorithm - are used as input data, and then the required parameters for the fuzzy clustering of SENTINEL-1A SAR image, were calculated. Lake Constance, Germany has been chosen as the study area. In this study, additionally an interface plugin has been developed and integrated into the open source Quantum GIS software platform. The developed interface allows non-experts to process and extract the shorelines without using any parameters. But, this system requires pre-segmented data as input. Thus, the batch process calculates the required parameters.