<p>Separation between water and land is vital for marine scientific research and coastal zone planning and management. The contrasting backscatter properties of land and ocean enable clear water edge line identification in synthetic aperture radar (SAR) imagery. However, SAR images are prone to speckle noise, and the complexity of the water-land boundaries environment makes accurate water-land separation challenging. To overcome noise and complex background interference in remote sensing images, an improved level set method was employed to enhance water-land separation. In the traditional distance regularized level set method, the selection of the image correlation weight coefficient and the edge indicator function directly influences the accuracy of the final segmentation results. A novel level set segmentation algorithm incorporating an improved edge indicator function is proposed to efficiently and accurately separate the water edge lines in SAR images. The distance regularized level set evolution model is enhanced by incorporating the signed pressure force function as an adaptive parameter, which serves as an external constraint for curve evolution. A novel level set model with an adaptive edge indicator function, combining gradient and regional information, is proposed. Experimental results demonstrate that the proposed model enhances the accuracy of waterland separation in SAR images. However, further research is needed to evaluate its potential for detecting boundaries in diverse marine environments and across different types of SAR imagery.</p>