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 subset of a large data set of close‐range, high‐resolution imagery collected from cameras aboard an icebreaker during an Antarctic winter expedition. Spanning a wide range of ice conditions and image clarity in the MIZ, the subset provides a rigorous segmentation test bed. Performance is assessed in terms of floe detection accuracy, size distribution, and ice concentration, with results compared against a manually segmented benchmark. Results indicate SAM, in prompt‐driven mode, offers the best balance between accuracy and computational efficiency. Its strong performance in estimating sea ice concentration and detecting floes, while maintaining close agreement with benchmark floe size distributions, makes it suitable for real‐time applications and scalable analyses of large imagery data sets. Compared with SAM, the combined SAM‐GVF method provides more accurate floe boundary delineation, although at much higher computational cost, and is therefore better suited for analyses requiring precise floe shapes.
Passerotti et al. (Sun,) studied this question.
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