海冰外缘线是一个描述北极海冰快速变化的重要指示参数,对近岸冰区航行保障和海冰灾害预警具有实际意义。本文首先基于形态学中的方法分别识别数据中的主体冰域、主体水域和碎冰区。其次利用可变图像闭运算方法将较大碎冰与主体冰区合并,最后再利用连通域方法提取海冰外缘线。该方法可适用于任何冰水二值数据集,包括海冰密集度产品数据、卫星图像、航拍图像以及其他冰水混合数据。本文基于AMSR-E海冰密集度数据,利用此方法提取了北冰洋10个区域的海冰外缘线,与15%海冰密集度等值线比较表明,本文方法能够保留较大面积的碎冰区域,并将其与主体冰域合并处理,因此所提取的海冰外缘线在衡量大尺度海冰范围方面更为合理。

Sea ice edge is an important index for illustrating changes in the Arctic sea ice. This parameter is important for navigation safety and sea ice disaster warning in Chinese northern coast. Sea ice edge is often previously obtained through long-term artificial judgment or in default equivalent to a certain isoline of sea ice concentration, which neglects the influence of large floes on sea ice edge. This study aims to develop a more accurate and faster automatic method for monitoring sea ice edge compared with conventional monitoring techniques. A novel method based on morphology is proposed. Connected Component Analysis(CCA) and image closing operation are performed. The main ice region, main water region, and floes are distinguished using CCA twice. Then, some large floes are reserved and merged into the principal ice region. Finally,sea ice edge is retrieved by a changeable image closing operation with self-adaptive structural element. This method can be extensively applied to ice-water binary data, such as sea ice concentration, satellite image, and aerial image. The proposed technique is applied on the reflectance data produced from band 1 and band 2 of MODIS(Moderate-resolution Imaging Spectroradiometer). The new method is used to retrieve sea ice edge from sea ice concentration data from advanced microwave scanning radiometer for EOS data covering ten regions in the Arctic ocean. Compared with the 15% isoline of sea ice concentration in the regions covered by many large ice floes, the sea ice edge obtained by the new method is more reasonable for monitoring large-scale sea ice. This advantage is caused by the reservation and merging of slightly large floes into the principal ice region.Rapid monitoring of sea ice in the Chinese northern coast without the restriction of chosen data formatis feasible because the method is automatic and can be widely applied. Meanwhile, the dramatic change in the Arctic sea ice can be quantified using the sea ice edge obtained by the proposed method. A regional and integral Arctic sea ice edge dataset can be built for further Arctic studies.