Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching
文献类型:期刊论文
作者 | Yu C(余创)3,4,5,6; Liu YP(刘云鹏)5; Li CX(李晨曦)5; Qi L(亓琳)4; Xia X(夏鑫)5; Liu TC(刘天赐)5; Hu ZH(胡祝华)3 |
刊名 | IEEE Transactions on Geoscience and Remote Sensing |
出版日期 | 2022 |
卷号 | 60页码:1-15 |
ISSN号 | 0196-2892 |
关键词 | Combined metric network Cross-spectral image patch matching Feature difference multibranch feature difference learning network (MFD-Net) |
产权排序 | 1 |
英文摘要 | Cross-spectral image patch matching is still challenging due to significant nonlinear differences between image patches. Recently, image patch matching methods based on feature relation learning have attracted increasing attention and achieved good performance. However, we find that the metric learning methods based on feature difference cannot comprehensively and effectively extract useful discriminative information between image patch pairs by only adopting two branches network structure. Therefore, we propose a novel multi-branch feature difference learning network (MFD-Net). Specifically, we build a multi-branch parallel feature difference extraction network, which can capture richer and more discriminative feature difference information and achieve significant improvements on matching tasks. Furthermore, we propose a combined metric network composed of a master metric network module and multiple branch metric network modules, which promotes the forward update of network weights and reduces the similarity of features extracted by each feature difference extraction module with negligible increase in inference time. Extensive experimental results show that the proposed MFD-Net achieves superior performances on cross-spectral image patch matching and single spectral image patch matching. |
语种 | 英语 |
资助机构 | Innovation Project of Equipment Development Department—Information Perception Technology under Grant E01Z040601 |
源URL | [http://ir.sia.cn/handle/173321/31019] |
专题 | 沈阳自动化研究所_光电信息技术研究室 |
通讯作者 | Liu YP(刘云鹏) |
作者单位 | 1.School of Information and Communication Engineering, Hainan University, Haikou 570228, China 2.School of Mechanical and Vehicle Engineering, Linyi University, Linyi 276000, China 3.University of Chinese Academy of Sciences, Beijing 100049, China 4.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 6.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China |
推荐引用方式 GB/T 7714 | Yu C,Liu YP,Li CX,et al. Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching[J]. IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-15. |
APA | Yu C.,Liu YP.,Li CX.,Qi L.,Xia X.,...&Hu ZH.(2022).Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching.IEEE Transactions on Geoscience and Remote Sensing,60,1-15. |
MLA | Yu C,et al."Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching".IEEE Transactions on Geoscience and Remote Sensing 60(2022):1-15. |
入库方式: OAI收割
来源:沈阳自动化研究所
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