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
图像处理 海冰漂移 合成孔径雷达 三角剖分 特征跟踪 模式匹配 image processing sea-ice drift synthetic aperture radar triangulation feature tracking pattern matching
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.