激光与光电子学进展, 2019, 56 (16): 161005, 网络出版: 2019-08-05  

特征跟踪与模式匹配结合算法在海冰漂移检测中的应用研究 下载: 939次

Combined Feature-Tracking and Pattern-Matching Algorithm for Sea-Ice Drift Detection
作者单位
1 内蒙古科技大学信息工程学院内蒙古自治区模式识别与智能图像处理重点实验室, 内蒙古 包头 014010
2 内蒙古工业大学, 内蒙古 呼和浩特 010051
3 自然资源部第一海洋研究所, 山东 青岛 266061
摘要
基于Sentinel-1遥感数据,采用有效的预处理方法,得到较为准确的数据集,并提出一种基于三角剖分的特征跟踪与模式匹配结合算法,通过建立三角形网络并有效结合两者的优势,既提高了算法效率,又使海冰漂移矢量具有更均匀的空间分布。同时,研究了HH极化及其数据强噪声区域下该算法的适用性。不同极化数据的实验结果显示,本文算法所得海冰漂移矢量不仅有更高的覆盖率,而且均方根误差降低了约10%,提高了检测精度。面对噪声稳健性的增强,即使在条带噪声的干扰下,检测准确率仍可高达98%,可见该算法对两种极化方式具有普适性,从而证明该方法能够有效地应用于海冰漂移监测。
Abstract
We introduced an effective preprocessing method based on Sentinel-1 remote-sensing data to obtain a more accurate dataset and proposed a triangulation-based feature-tracking and pattern-matching algorithm. By establishing a triangular network, the advantages of the two algorithms were effectively combined, which not only improved the efficiency but also enhanced the spatial-distribution uniformity of the sea-ice drift vectors. Additionally, this study investigated the applicability of the algorithm for strong-noise areas of like-polarized (HH) and cross-polarized (HV) data. The experimental results show that sea-ice drift vectors obtained using this algorithm exhibit a high coverage and reduce the root mean square error by ~10%, thereby improving the detection accuracy and robustness against noise. Furthermore, the detection accuracy remains as high as 98%, even in the presence of interference by strip noise. These results demonstrate the effectiveness of this method for effectively monitoring sea-ice drift.

王军凯, 吕晓琪, 张明, 李菁, 孟娴静, 刘根旺. 特征跟踪与模式匹配结合算法在海冰漂移检测中的应用研究[J]. 激光与光电子学进展, 2019, 56(16): 161005. Junkai Wang, Xiaoqi Lü, Ming Zhang, Jing Li, Xianjing Meng, Genwang Liu. Combined Feature-Tracking and Pattern-Matching Algorithm for Sea-Ice Drift Detection[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161005.

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