Sea Ice Floe Segmentation in Close-Range Optical Imagery Using Active Contour and Foundation Models

Abstract

The size of sea ice floes in the marginal ice zone (MIZ) is a key factor influencing ice coverage, albedo, wave propagation, and ocean–atmosphere energy exchanges. Floe size can be observed by processing visual-range imagery from ships, aircraft, or satellites. However, autonomously capturing floe boundaries remains challenging, particularly due to sea ice heterogeneity, which impairs boundary definition and reduces image clarity. This study evaluates the accuracy of sea ice floe segmentation using the gradient vector flow (GVF) active contour method, the deep learning-based Segment Anything Model (SAM), and a hybrid approach combining GVF and SAM. Methods are evaluated on a representative s..