Research on Coastline Detection Method of SAR Image Based on Seed Point Growth

In recent years, more and more SAR image technology has been used in the field of sea area management. Among them, the extraction of coastline is one of the hot research issues. During the propagation of electromagnetic waves, due to the inhomogeneity, discontinuity, anisotropy and loss of the medium. Satellite remote sensing technology has the following advantages. First of all, compared with traditional manual operations, satellite remote sensing has the ability of fast collection and wide search range, and can simultaneously observe multiple areas in a relatively short period of time, effectively grasping the instantaneous status of ground or space targets. Although not affected by light intensity, SAR images suffer from inherent speckle noise. Different land geological structures and ground features have complex scattering characteristics. On the ocean, sea wind, whirlpools, tides, etc. will also change the reflection of radar waves. Therefore, it is necessary to use tools such as computers to accelerate the processing of data through automatic identification technology and target feature extraction technology. SAR image processing is such a technology that meets the requirements of large-scale data processing. On land, the radio wave scattering characteristics of different regions will be different, resulting in rich gray levels of land targets. Because the sea area obtained by the algorithm based on seed point growth is connected, it can effectively avoid the interference of noise spots in SAR image, so as to make the detection more stable and robust. On the ocean, due to the factors such as internal wave, vortex and ocean current caused by sea wind, the sea surface presents varying degrees of roughness, resulting in large differences in reflection coefficients. In this way, if a simple method is used to detect the coastline, it is bound to cause great difficulties. Aiming at the idea of seed point growth, this paper presents a fast coastline detection algorithm.