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Estimation of degree of sea ice ridging based on dual-polarized C-band SAR data

机译:基于双极化C波段SAR数据的海冰起伏程度估算

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For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81?% in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.
机译:对于在波罗的海冰中航行的船舶,通常每天在手动准备的冰图中报告冰边缘,冰浓度,冰厚度和起伏程度等参数。这些图表为破冰船提供了路线优化和燃料计算的基本信息。但是,手动制冰需要较长的分析时间,因此无法对大面积区域(例如北冰洋)进行详细分析。在此,我们基于RADARSAT-2 C波段双极化(HH / HV通道)SAR纹理特征和从芬兰冰中提取的海冰浓度信息,提出了一种自动估算波罗的海地区冰凌程度的方法图表。首先分割SAR图像,然后为每个片段提取几个纹理特征。使用随机森林方法,我们将它们分为四类起伏强度,并将它们与从数字化冰图提取的参考数据进行比较。基于冰图的起冰程度与自动化结果之间的总体协议每月变化,分别在2013年1月,2月和2013年3月分别为83%,63%和81%。利用2011年3月的一次野外活动收集的数据,验证了冰图上报告的冰凌起伏程度与实际海脊密度之间的对应关系。原则上,该方法可以应用于北冰洋的季节性海冰状况。

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