Review of waterline extraction methods based on remote sensing images
The coastline is the key to reflecting the impact of natural factors and human activities on the coastal environment,and holds significant practical value in fields such as coastal resource development and coastal zone comprehensive management.The process of extracting the coastline based on remote sensing technology typically involves first extracting the instantaneous water edge using remote sensing imagery and then correcting it for tidal variations.This article summarizes the waterline extraction methods,which are critical for the coastline analysis,mainly introducing the traditional and artificial intelligence methods for extracting waterline from remote sensing images.The traditional methods include threshold segmentation,region segmentation,edge detection,and object-oriented methods.The artificial intelligence methods include traditional machine learning and deep learning.On the basis of summarizing and analyzing the characteristics,advantages and disadvantages of various specific waterline extraction methods,this article looks forward to the future research directions of waterline extraction.This article believes that in the future,the use of remote sensing images for waterline extraction can focus on fine extraction of waterline based on high-resolution images,fully automatic extraction of waterline based on deep learning methods,multi category extraction of waterline from different coasts,and application of waterline extraction based on drone remote sensing.
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