太赫兹科学与电子信息学报2026，Vol.24Issue(8)：909-918,10.DOI:10.11805/TKYDA2025202
EnhancedXFeat:一种SAR图像和光学图像配准方法
EnhancedXFeat:a registration method for SAR image and optical image
周郁植 1张海洋 1李元吉 2辛乐 2赵长明1
作者信息
- 1. 光电成像技术与系统教育部重点实验室,北京 100081||北京理工大学 光电学院,北京 100081
- 2. 南京电子技术研究所,江苏 南京 210039||雷达探测感知全国重点实验室,江苏 南京 210039
- 折叠
摘要
Abstract
To address the cross-modal registration challenges between Synthetic Aperture Radar(SAR)and optical images,which arise from differences in imaging mechanisms,noise characteristics,and textures—particularly the difficulties in feature extraction from low-texture SAR images,significant domain disparities,and high computational costs of existing methods—this study proposes an improved algorithm named EnhancedXFeat based on the Accelerated Features(XFeat)network.The algorithm enhances the ability to preserve shallow-level details and distinguish deep-level semantics by doubling the number of channels in key modules of the backbone network;introduces a cross-modal dual-branch structure to independently process modality-specific features;and applies a dual attention mechanism twice.These designs effectively resolve the domain difference issue and improve the performance of feature extraction and fusion for low-texture images.Comparative experimental results on a self-constructed dataset demonstrate that EnhancedXFeat outperforms mainstream algorithms significantly:the average number of detected feature points reaches 37.0,which is much higher than 4.3 of Scale-Invariant Feature Transform(SIFT),6.2 of Oriented FAST and Rotated BRIEF(ORB),and 17.9 of Optical-SAR Match Net(OSMNet);the matching accuracy achieves 88.1%,remarkably superior to 9.2%of SIFT,12.7%of ORB,68.0%of OSMNet and 68.7%of VGG-16;meanwhile,the single-operation time is controlled to 0.801 2 s,achieving a favorable balance between accuracy and efficiency.The conclusion indicates that EnhancedXFeat effectively improves the accuracy and robustness of SAR-optical image registration through its targeted design,and its lightweight architecture provides an efficient and reliable solution for multi-source remote sensing image registration applications in resource-constrained environments.关键词
图像配准/合成孔径雷达(SAR)/跨模态融合Key words
image registration/Synthetic Aperture Radar(SAR)/cross-modal fusion分类
信息技术与安全科学引用本文复制引用
周郁植,张海洋,李元吉,辛乐,赵长明..EnhancedXFeat:一种SAR图像和光学图像配准方法[J].太赫兹科学与电子信息学报,2026,24(8):909-918,10.
