基于深度特征融合的SAR图像与AIS信息关联方法  

An association method between SAR images and AIS information based on depth feature fusion

作　　者：李浩然 熊伟 崔亚奇 LI Haoran;XIONG Wei;CUI Yaqi(Research Institute of Information Fusion,Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学信息融合研究所,山东烟台264001

出　　处：《系统工程与电子技术》2023年第11期3491-3497,共7页Systems Engineering and Electronics

摘　　要：星载合成孔径雷达(synthetic aperture radar,SAR)和自动识别系统(automatic identification system,AIS)都可以获取到探测目标的相关信息,将两者获取的信息进行关联融合,有益于实现高效的海上侦察监视。由于数据之间存在的异构性,传统方法多依赖人工特征建立SAR图像与AIS信息的关联关系,但这些方法存在精度差、效率低等缺点。本文提出了一种基于深度特征融合的SAR图像与AIS信息关联方法,针对两种模态数据的特点分别设计了对应的特征学习网络获取单模态特征表示,进一步融合不同模态的特征信息以增强跨模态信息间的语义相关性,然后通过设计的关联学习目标函数进行跨模态特征之间关联学习。在构建的数据集上验证表明,所提方法关联精度高、适应性强,验证了所提数据集和方法的有效性。Spaceborne synthetic aperture radar(SAR)and automatic identification system(AIS)can obtain the information of the detected target.And the association and fusion of the information obtained from the two sensors is beneficial to realize efficient maritime reconnaissance and surveillance.Due to the heterogeneity gas between different data,traditional methods mostly rely on artificial features to establish correlation between SAR images and AIS information,but these methods have disadvantages of poor accuracy and low efficiency.In this paper,the association method between SAR images and AIS information based on depth feature fusion is proposed.According to the characteristics of the two modal data,the corresponding feature learning network is designed respectively to obtain the unimodal feature representation,and the feature information of different modals is further fused to enhance the semantic correlation of cross-modal information.Then,the association learning between cross-modal features is carried out by the designed association learning objective function.Verification on the constructed dataset shows that the proposed method can achieve high correlation accuracy and strong adaptability,and the effectiveness of the proposed dataset and method is verified.

关 键 词：多源数据关联 深度学习 遥感图像 跨模态检索 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]