An Indicator and Min-Cost Approach for Shoreline Extraction From Satellite Imagery in Muddy Coasts

Shoreline location plays a key role in coastal research, management, and engineering. Remote sensing enables the quantification of shoreline information with the large spatial extent and high temporal frequency. Driven by river discharge and ocean dynamics, muddy coasts exhibit complicated spatiotemporal variations. It is essential, yet challenging to extract effective shoreline features from satellite images. Taking the Yellow River Delta coast in China as the study area, we present an indicator and min-cost approach to extract the shoreline in muddy coasts. The shoreline is represented as a set of linearly connected central points with high shoreline probabilities, and a set of image and spatial indicators are developed to assess these probabilities. The Salient Value indicator integrates the gradient magnitude and the edge intensity to detect the boundary strength; the Regional Difference indicator separates the water/land class from edge intensity to measure the possibility of being water or land; and the Seaward Distance indicator spatially distinguishes the true shoreline from other spectrally similar boundaries. A cost function combines these indicators to evaluate the local shoreline possibilities. A shoreline set is produced by an improved min-cost path method to evaluate the overall shoreline possibilities. The optimal shoreline paths are selected based on the parameter analyses of the shoreline set. The performance of the approach is confirmed by comparing with the ground truth and state-of-the-art methods. The effectiveness of the approach is tested for different spatial resolution data and coastal environments.