Yu Wei, You Hongjian, Lv Peng, Hu Yuxin, Han Bing
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2021 Nov 13;21(22):7547. doi: 10.3390/s21227547.
Geostationary optical remote sensing satellites, such as the GF-4, have a high temporal resolution and wide coverage, which enables the continuous tracking and observation of ship targets over a large range. However, the ship targets in the images are usually small and dim and the images are easily affected by clouds, islands and other factors, which make it difficult to detect the ship targets. This paper proposes a new method for detecting ships moving on the sea surface using GF-4 satellite images. First, the adaptive nonlinear gray stretch (ANGS) method was used to enhance the image and highlight small and dim ship targets. Second, a multi-scale dual-neighbor difference contrast measure (MDDCM) method was designed to enable detection of the position of the candidate ship target. The shape characteristics of each candidate area were analyzed to remove false ship targets. Finally, the joint probability data association (JPDA) method was used for multi-frame data association and tracking. Our results suggest that the proposed method can effectively detect and track moving ship targets in GF-4 satellite optical remote sensing images, with better detection performance than other classical methods.
地球静止轨道光学遥感卫星，如高分四号卫星，具有高时间分辨率和宽覆盖范围，能够对大范围的船舶目标进行连续跟踪和观测。然而，图像中的船舶目标通常较小且较暗，并且图像容易受到云层、岛屿等因素的影响，这使得船舶目标的检测变得困难。本文提出了一种利用高分四号卫星图像检测海面行驶船舶的新方法。首先，采用自适应非线性灰度拉伸（ANGS）方法增强图像，突出小而暗的船舶目标。其次，设计了一种多尺度双邻域差分对比度测量（MDDCM）方法来检测候选船舶目标的位置。分析每个候选区域的形状特征以去除虚假船舶目标。最后，使用联合概率数据关联（JPDA）方法进行多帧数据关联和跟踪。我们的结果表明，所提出的方法能够有效地检测和跟踪高分四号卫星光学遥感图像中的移动船舶目标，其检测性能优于其他经典方法。
