A supervised method combining seeds learning and graph cut (GC) is proposed to address the problem of sea–land segmentation.

We propose a multi-feature descriptor to describe the sea and the land, classification results on testing samples demonstrate the effectiveness of the descriptor.

Superpixel method is used to extract samples and build graph model for GC, which will reduce information redundancy and enhance the local clustering property of neighboring pixels.

Edge information between neighboring superpixels is incorporated when building the boundary term of GC to reduce the cost of separating superpixels at two sides of the edge into different parts, which can help to avoid under-segmentation for some thin and elongated structures.

Segmentation results of our method outperform that of state-of-the-art methods in terms of quantitative and visual performance.