Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery

Hongxia Zheng, Xiao Li, Jianhua Wan (), Mingming Xu, Shanwei Liu and Muhammad Yasir
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Hongxia Zheng: College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
Xiao Li: Qilu Aerospace Information Research Institute, Jinan 250100, China
Jianhua Wan: College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
Mingming Xu: College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
Shanwei Liu: College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
Muhammad Yasir: College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China

Sustainability, 2023, vol. 15, issue 9, 1-16

Abstract: Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels.

Keywords: SAR image; instantaneous waterline; coastline extraction; KNN method; correction criteria (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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