Remote sensing images can obtain broad geomorphic features and provide a strong basis for analysis and decision making. As 71% of the earth is covered by water, shipping has become an efficient means of international trade and transportation, and the development level of coastal cities will directly reflect the development level of a country. The coastline is the boundary line between seawater and land, so it is of great significance to accurately identify it to assist shipping traffic and docking, and this identification will also play a certain auxiliary role in environmental analysis. Currently, the main problems of coastline recognition conducted by remote sensing images include: (1) in the process of remote sensing, image transmission inevitably brings noise causing poor image quality and difficult image quality enhancement; (2) s single scale does not allow for the identification of coastlines at different scales; and (3) features are under-utilized, false detection is high and intuitive measurement is difficult. To address these issues, we used the following multispectral methods: (1) a PCA-based image enhancement algorithm was proposed to improve image quality; (2) a dual attention network and HRnet network were proposed to extract suspected coastlines from different levels; and (3) a decision set fusion approach was proposed to transform the coastline identification problem into a probabilistic problem for coastline extraction. Finally, we constructed a coastline straightening model to visualize and analyze the recognition effect. Experiments showed that the algorithm has an AOM greater than 0.88 and can achieve coastline extraction.