摘要
为解决合成孔径雷达影像和可见光影像融合过程中存在信息丢失、细节模糊,造成目标检测与识别精度不足的问题,提出一种贯穿像素、特征、决策3个层级的多级图像融合方法。首先,在像素级别上初步融合,提高检测模型对纹理和形态上细节信息的敏感度。之后,在特征级别上进一步融合,使模型更关注全局的语义信息,捕捉复杂背景下的目标特征。最后,在决策级别上整合融合信息并筛选出具有高可信度和精确度的检测结果。模型在自制目标识别数据集上进行了对比实验和消融实验,实验结果验证了其有效性。
To address the issues of information loss and detail blurring in the fusion process of synthetic aperture radar(SAR)and visible light images,which lead to insufficient accuracy in target detection and recognition,a multi-level image fusion method spanning pixel,feature,and decision levels is proposed.Firstly,perform preliminary fusion at the pixel level to enhance the sensitivity of the detection model to texture and morphological details.Then,conduct further fusion at the feature level to make the model focus more on global semantic information and capture target features in complex backgrounds.Finally,integrate and fuse the information at the decision level,selecting detection results with high reliability and accuracy.The model is compared and ablated experiments on a self-constructed target recognition dataset,with experimental results confirming its effectiveness.
作者
翟佳
画麒嘉
王梓权
张子恺
王睿琦
刘金玲
胡美琪
武晨辉
ZHAI Jia;HUA Qijia;WANG Ziquan;ZHANG Zikai;WANG Ruiqi;LIU Jinling;HU Meiqi;WU Chenhui(School of Information and Communication Engineering,Communication University of China,Beijing 100024,China;National Key Laboratory of Scattering and Radiation,Beijing 100854,China;International School of Information Science&Engineering,Dalian University of Technology,Dalian 116081,China)
出处
《系统工程与电子技术》
北大核心
2026年第3期817-825,共9页
Systems Engineering and Electronics
关键词
多级别融合
联合识别
合成孔径雷达
可见光
multi-level fusion
joint recognition
synthetic aperture radar(SAR)
visible light
