Multinetwork Algorithm for Coastal Line Segmentation in Remote Sensing Images

The demarcation between the sea and the land, commonly referred to as the coastline, is of paramount importance for the dynamic monitoring of its alterations. This monitoring is essential for the effective utilization of marine resources and the conservation of the ecological environment. Addressing the challenges posed by the extensive expanse of coastal lines, which can complicate their acquisition and processing, this study utilizes remote sensing imagery to introduce an algorithm for coastal line segmentation. The algorithm integrates multiple networks to enhance its effectiveness. Innovations encompass the development of an extraction algorithm for coastal lines that are as follows. First, utilize an attention-guided conditional generative adversarial network (AC-GAN) model, which redefines the task of image segmentation by framing it as a style transformation problem. Second, a strategy for coastal line segmentation utilizes Dense Swin Transformer Unet (DSTUnet) to construct a densely structured model. This approach integrates Transformer to prioritize focal regions, thereby enhancing image and semantic interpretation. Third, a transfer learning framework is proposed to integrate multiple features, leveraging the strengths of different networks to achieve accurate segmentation of coastal lines. The study introduced two datasets, and the experimental results confirm that parallel network configurations and asymmetric weighting are superior in achieving optimal results, with an area overlap measure (AOM) score of 85%, outperforming the Unet by 5%.