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

Passerotti, Giulio, Alberello, Alberto, Vichi, Marcello, Bennetts, Luke G., Bailey, James and Toffoli, Alessandro (2026) Sea Ice Floe Segmentation in Close-Range Optical Imagery Using Active Contour and Foundation Models. Earth and Space Science, 13 (2). ISSN 2333-5084

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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.

Item Type:	Article
Additional Information:	Data Availability Statement: The 20 sample images used in this study, along with the image processing scripts, are available on GitHub (Passerotti, 2025).
Uncontrolled Keywords:	sea ice,computer vision,active contours,foundation model
Faculty \ School:	Faculty of Science > School of Engineering, Mathematics and Physics
UEA Research Groups:	Faculty of Science > Research Groups > Fluids & Structures
Related URLs:	http://www.scopus.com/inward/record.url?... https://agupubs.onlinelibrary.wiley.com/...
Depositing User:	LivePure Connector
Date Deposited:	09 Feb 2026 14:36
Last Modified:	23 Feb 2026 10:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101873
DOI:	10.1029/2025EA004453

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