Abstract

Abstract. Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

Highlights

  • There are many studies regarding extraction of coastlines from Synthetic Aperture Radar (SAR) data in recent years

  • Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy unsupervised approach

  • Classification refers to the fuzzy clustering using mean standard deviation method

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Summary

INTRODUCTION

There are many studies regarding extraction of coastlines from SAR data in recent years. Acar et al (2012) develop an algorithm that can extract coastlines automatically by using SAR images using fitcoast algorithm. They evaluated the results with manual measurements. Lee and Jurkevich ( 1990) tried to extract coastlines by using an edge-tracing method from low- resolution Synthetic Aperture Radar (SAR) images with resulting rough coastlines. Liu et al(2016) combine the modified K-means method and adaptive object-based region-merging mechanism (MKAORM) from wide-swath Synthetic Aperture Radar (SAR) images to extract the coastlines. Schmitt et al (2015) developed an automatic technique for coastline detection by implementing the active contours algorithm. Asaka et al (2013) developed an automated method for tracing shorelines in L-band SAR images with three steps. Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy unsupervised approach

USED DATA
Classification
CONCLUSIONS
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