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A practical algorithm for the retrieval of floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar imagery

机译:一种从高分辨率卫星合成孔径雷达图像中检索北极海冰絮凝物大小分布的实用算法

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In this study, we present an algorithm for summer sea ice conditions that semi-automatically produces the floe size distribution of Arctic sea ice from high-resolution satellite Synthetic Aperture Radar data. Currently, floe size distribution data from satellite images are very rare in the literature, mainly due to the lack of a reliable algorithm to produce such data. Here, we developed the algorithm by combining various image analysis methods, including Kernel Graph Cuts, distance transformation and watershed transformation, and a rule-based boundary revalidation. The developed algorithm has been validated against the ground truth that was extracted manually with the aid of 1-m resolution visible satellite data. Comprehensive validation analysis has shown both perspectives and limitations. The algorithm tends to fail to detect small floes (mostly less than 100 m in mean caliper diameter) compared to ground truth, which is mainly due to limitations in water-ice segmentation. Some variability in the power law exponent of floe size distribution is observed due to the effects of control parameters in the process of de-noising, Kernel Graph Cuts segmentation, thresholds for boundary revalidation and image resolution. Nonetheless, the algorithm, for floes larger than 100 m, has shown a reasonable agreement with ground truth under various selections of these control parameters. Considering that the coverage and spatial resolution of satellite Synthetic Aperture Radar data have increased significantly in recent years, the developed algorithm opens a new possibility to produce large volumes of floe size distribution data, which is essential for improving our understanding and prediction of the Arctic sea ice cover
机译:在这项研究中,我们提出了一种夏季海冰条件的算法,该算法可从高分辨率卫星“合成孔径雷达”数据中半自动生成北极海冰的絮凝物大小分布。当前,来自卫星图像的絮凝物粒度分布数据在文献中非常罕见,这主要是由于缺乏可靠的算法来产生此类数据。在这里,我们通过结合各种图像分析方法(包括核图割,距离变换和分水岭变换以及基于规则的边界重新验证)来开发算法。已针对地面真相验证了开发的算法,该地面真相是借助1米分辨率的可见卫星数据手动提取的。全面的验证分析显示了观点和局限性。与地面实况相比,该算法往往无法检测到小絮凝物(平均卡尺直径通常小于100 m),这主要是由于水冰分割的局限性所致。由于在去噪,内核图割分割,边界重新验证的阈值和图像分辨率的过程中,控制参数的影响,絮凝物尺寸分布的幂律指数出现了一些变化。但是,对于絮凝大于100 m的算法,在这些控制参数的各种选择下,已经显示出与地面真相的合理一致性。考虑到近年来卫星合成孔径雷达数据的覆盖范围和空间分辨率已显着提高,因此开发的算法为产生大量浮尘尺寸分布数据开辟了新的可能性,这对于增进我们对北极海的了解和预测至关重要冰盖

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