Improved Accuracy of Velocity Estimation for Cruising Ships by Temporal Differences Between Two Extreme Sublook Images of ALOS-2 Spotlight SAR Images With Long Integration Times
Improved Accuracy of Velocity Estimation for Cruising Ships by Temporal Differences Between Two Extreme Sublook Images of ALOS-2 Spotlight SAR Images With Long Integration Times
复制标题
利用长积分时间的 ALOS-2 聚光 SAR 图像的两个极端子视图像之间的时间差异提高巡航船速度估计的精度
DOI：
10.1109/jstars.2021.3127214
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
Ouchi Kazuo
中科院分区：
文献类型：
--
作者：
Yoshida Takero;Ouchi Kazuo
提出了一种利用合成孔径雷达（SAR）子图像在聚束模式下提高巡航舰船航速估计精度的方法。聚光SAR的主要目的是利用比其他成像模式更长的积分时间来获得高分辨率，而本文提出的方法正是基于这种长积分时间。主要的方法是产生巡航船的连续子图像，其中的系数大于10。look-1和look- n子图像的位置差异与巡航速度和长间隔时间差异成正比。从这两种子图像的互相关函数可以计算出巡航船的距离，从而计算出巡航船的速度，与其他模式相比，精度提高了。我们使用n = 2、10和20的PALSAR-2聚光灯子图像进行测试，并将结果与自动识别系统数据进行比较。测试了五幅接近方位方向的舰船图像；结果表明，10幅图像的平均误差为13.8%，20幅和2幅图像的平均误差分别为17.9%和40.5%。还给出了10次查看比20次查看效果更好的原因。
A method for improving the estimation accuracy of the velocity of cruising ships is proposed using synthetic aperture radar (SAR) sublook images in the spotlight mode. The main purpose of spotlight SAR is to obtain high resolution utilizing longer integration times than those of other imaging modes, and the proposed method is based on these long integration times. The principal methodology is to produce successiveNsublook images of a cruising ship, whereNis more than approximately 10. The positions of the look-1 and look-Nsubimages differ by a substantial distance proportional to the cruising speed and the long interlook time difference. The distance, and hence the velocity of the cruising ship, can be computed from the cross-correlation function of these two sublook images with improved accuracy compared with other modes. We tested using PALSAR-2 spotlight subimages withN= 2, 10, and 20, and the results are compared with the automatic identification system data. Five images of ships cruising close to the azimuth direction were tested; the best result was obtained for the 10-look images with an average error of 13.8%, followed by 17.9% and 40.5% errors for the 20- and 2-look images, respectively. The reason is also given for the best result of the 10-look case over the 20-look case.