利用主动轮廓和基础模型进行近距光学影像中海冰浮冰分割
Sea ice floe segmentation in close-range optical imagery using active contour and foundation models
AI总结：
本文提出利用SAM和GVF结合的方法进行海冰浮冰分割，通过近距影像数据集评估，SAM在准确性和效率上表现最佳，而混合方法更适用于需要精确边界定义的场景。
摘要：
边际海冰区（MIZ）中海冰浮冰的尺寸是影响冰盖范围、反照率、波浪传播和海洋-大气能量交换的关键因素。通过船舶、飞机或卫星的近距影像处理可以观测到浮冰的尺寸。然而，自动捕捉浮冰边界仍然具有挑战性，特别是由于海冰的异质性，这会损害边界定义并降低图像清晰度。本研究评估了使用梯度向量流（GVF）主动轮廓方法、基于深度学习的Segment Anything Model（SAM）以及结合GVF和SAM的混合方法对海冰浮冰分割的准确性。方法在从破冰船上的相机收集的高分辨率近距影像大型数据集的代表性子集中进行了评估。该子集涵盖了MIZ广泛范围的冰况和图像清晰度，提供了一个严谨的分割测试平台。性能在浮冰检测准确性、尺寸分布和冰浓度方面进行评估，并与手动分割的基准进行比较。结果表明，在提示驱动模式下，SAM在准确性和计算效率之间提供了最佳平衡。其在估计海冰浓度和检测浮冰方面的强大表现，同时保持与基准浮冰尺寸分布的接近一致，使其适合实时应用和大规模影像数据集的可扩展分析。与SAM相比，结合SAM-GVF的方法提供了更准确的浮冰边界定义，尽管计算成本显著更高，因此更适合需要精确浮冰形状分析的场合。
英文摘要：
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 dataset 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 datasets. 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.
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