Research Institute of Information Fusion, Naval Aviation University, Yantai 264001, China.
School of Electronic Science, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2018 Jun 22;18(7):2007. doi: 10.3390/s18072007.
The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.
静止轨道遥感卫星具有宽幅扫描、持续观测和快速响应的能力，在海上目标监测方面具有巨大潜力。GF-4 卫星是中国第一颗具有中分辨率的静止轨道光学遥感卫星。本文提出了一种基于 GF-4 卫星序列图像的船舶跟踪新方法。该算法分为三个阶段。首先，利用局部峰值信噪比（PSNR）的局部视觉显著图检测 GF-4 卫星序列图像中单帧中的船舶。其次，利用船舶的有理多项式系数（RPCs）和自动识别系统（AIS）数据进行动态校正，实现每个潜在目标的精确定位。最后，利用带幅度信息的改进多假设跟踪（MHT）算法进一步去除虚假目标，实现船舶的跟踪，并估计船舶的运动参数。该算法已经使用 GF-4 序列图像和 AIS 数据进行了测试。实验结果表明，该算法在 GF-4 卫星序列图像中具有良好的跟踪性能，并能准确估计船舶的运动信息。
