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海冰漂移对北极气候研究和人类活动保障有重要意义。对星载辐射计或散射计数据采用模板匹配法提取的海冰漂移矢量结果存在空间分辨率较低及精度不佳等问题。合成孔径雷达（SAR）影像具有更高的空间分辨率，结合特征匹配算法可用于提取高分辨率海冰漂移场。本文采用Sentinel-1影像为数据源，比较了四种常用的特征匹配算子SIFT、SURF、ORB、A-AKZE提取北极海冰漂移矢量的效果，并分析了HH与HV极化通道提取的漂移矢量提取的空间分布差异。针对特征匹配中不可避免的错误，结合既有算法的优势本研究提出了一套高效且准确的错误矢量滤除算法。本研究利用MOSAiC浮标定位数据验证了该方法提取的海冰漂移矢量的精度，并与基于Sentinel-1影像的既有海冰漂移产品进行了精度对比。实验结果表明基于A-KAZE算子的算法在提取结果的数量与分布上优于SIFT、SURF与ORB算子。HH与HV极化通道影像提取的海冰漂移矢量在数量与空间分布上有所差异，结合两者可有效扩大海冰监测范围。本文采用的错误矢量滤除算法能高效滤除错误匹配的同时保留更多正确漂移矢量。基于A-KAZE算子提取的海冰漂移矢量平均速度误差低于0.2 km/d，平均方向误差低于1°，与同样基于Sentinel-1 SAR影像但采用模板匹配法的DTU海冰漂移产品具有较高的一致性，但本方法提取的海冰漂移矢量具有更高的空间分辨率及更大的覆盖范围。
关键词:北极 海冰漂移矢量 图像特征匹配 A-KAZE Sentinel-1
Sea ice drift is an important natural phenomenon in the Arctic, which is of great significance for climate research and human activities such as shipping security in the Arctic area. At present, sea ice drift products are often derived with space-borne radiometer and scatterometer with template matching algorithm and suffered from low resolution and low accuracy. Sentinel-1 synthetic aperture radar imagery advantages of high spatial resolution, and holds great potential for deriving sea ice drift fields with high resolution and high accuracy by applying feature matching algorithms. Using two pairs of Sentinel-1 Arctic sea ice SAR images, this research compared sea ice drift results derived from four popular features including SIFT, SURF, ORB and A-KAZE, and analyzed the similarities and differences between HH and HV imagery in terms of spatial distribution and coverage of the derived sea ice drift vectors. Aiming at identifying wrong vectors after the NNDR test with high calculation efficiency and accuracy, we proposed a filtering method combined with two published methods. At last, we evaluated the sea ice drift vectors" accuracy by comparing our derived results and DTU sea ice products to GPS data of MOSAiC buoys. Employing A-KAZE features to Sentinel-1 EW imagery can effectively derive the sea ice drift fields with high spatial resolution and coverage rates. A-KAZE feature performs better than SIFT, SURF and ORB in terms of spatial distribution and the number of vectors. Combining the vectors obtained from HH and HV polarization imagery can effectively extend the coverage of sea ice motion fields. The wrong vector filter checks the similarity of a vector to its neighbours only if its speed or direction exceeds 2 times the standard deviation. It improves computational efficiency and retains more correct vectors compared to two traditional methods. Validation with MOSAiC buoys data found that the average speed error of sea ice drift vectors extracted with the proposed A-KAZE based method was less than 0.2 km/day, and the average direction error was less than 1 °, which shared a high consistency with DTU sea ice drift products which also employs Sentinel-1 SAR imagery but applying the template matching algorithm, but our proposed methods presented a higher spatial coverage. This study demonstrates the potential of deriving sea ice drift vectors by applying dual-polarized Sentinel-1 SAR imagery and A-KAZE features. It can effectively and quickly generate high spatial resolution sea ice drift vector fields with high spatial covering rates and high accuracy, which can serve as an accurate data source for climate research and maritime security in the Arctic.