基于深度特征的质量感知旋转舰船模板匹配算法

doi:10.19678/j.issn.1000-3428.0066579

摘要/Abstract

摘要：

天基高价值目标连续跟踪在态势预警、军事决策等方面有着广泛应用。目前基于遥感视频卫星的跟踪都是单星目标跟踪，由于低轨卫星过顶时间有限，因此难以进行连续跟踪。为了实现跨星接力跟踪，接力卫星在经过非连续时间的目标环境和观察视角变化后需要唯一地匹配出高价值目标，为此，采用卷积神经网络VGG19作为主干网络，提出一种基于深度特征的质量感知旋转舰船模板匹配算法。设计双级特征融合模块，通过融合不同深度的特征信息解决因舰船目标尺寸差异较大导致的匹配不准确问题；针对因环境变化导致的定位不准确问题，引入质量感知模板匹配模块；使用细粒度舰船角度定位模块，利用贝叶斯公式确定舰船方向信息，为跨星连续跟踪提供准确的模板。实验结果表明，该算法提高了舰船目标匹配准确率，相对于QATM、DDIS、SIFT方法，AUC分别提升了9.5、16.0和17.5个百分点，AP75分别提升了21.1、30.5和6.9个百分点，所提算法可以有效提高舰船模板匹配的精度，能够为实现卫星星座跨星连续跟踪提供技术支持。

关键词: 深度特征, 卷积神经网络, 模板匹配, 特征融合, 遥感图像

Abstract:

Space-based high-value-target continuous tracking has a wide range of applications in situation warning and military decision-making. Currently, single-satellite target tracking is used for the remote-sensing video satellite; however, it is difficult to track continuously because of the limited overhead time of low-earth-orbit satellite. Therefore, it is necessary for relay satellites to uniquely match high-value targets after noncontinuous time changes occur in the target environment and viewing angle to achieve cross-satellite relay tracking. This paper proposes a quality-aware rotating-ship template matching algorithm based on deep features using the Convolutional Neural Network(CNN)VGG19 backbone network. A dual-level feature fusion module is designed to solve the problem of inaccurate matching caused by significant differences in the ship target size by fusing feature information from different depths. A quality-aware template matching module is introduced to solve the problem of inaccurate positioning caused by environmental changes. Finally, the fine-grained ship angle-positioning module is used to determine the ship orientation information using the Bayesian formula, which provides an accurate template for cross-satellite continuous tracking. The experimental results show that the proposed method improves the accuracy of ship target matching. Compared with the AUC values obtained using the QATM, DDIS, and SIFT methods, the AUC of the proposed method increases by 9.5, 16.0, and 17.5 percentage points, respectively, and the AP75 increases by 21.1, 30.5, and 6.9 percentage points, respectively. The proposed method effectively improves the accuracy of ship template matching and is valuable for achieving the cross-satellite continuous tracking of satellite constellations.

Key words: deep features, Convolutional Neural Network(CNN), template matching, feature fusion, remote sensing images

王泽瑞, 陈实. 基于深度特征的质量感知旋转舰船模板匹配算法[J]. 计算机工程, 2023, 49(12): 161-168.

Zerui WANG, Shi CHEN. Quality-Aware Rotating-Ship Template Matching Algorithm Based on Deep Features[J]. Computer Engineering, 2023, 49(12): 161-168.

https://www.ecice06.com/CN/Y2023/V49/I12/161

图/表 12

图1 卫星星座接力跟踪处理流程

Fig.1 Relay tracking process of satellite constellation

图2 Rotated QATM方法流程

Fig.2 Procedure of the Rotated QATM method

图3 双级特征融合模块结构

Fig.3 Dual-level feature fusion module structure

图4 目标定位方法

Fig.4 Target positioning method

图5 旋转角度定位方法

Fig.5 Rotation angle positioning method

图6 4种算法的准确率曲线

Fig.6 Accuracy curves of four algorithms

图7 4种算法的PR曲线对比

Fig.7 Comparison of PR curves of four algorithms

图8 不同特征层匹配的ROC曲线对比

Fig.8 Comparison of ROC curves of different feature layer matching

图9 3组存在典型干扰的匹配对的实验结果

Fig.9 Experimental results of three paired pairs with typical interference

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