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Multibranch Feature Difference Learning Network for Cross-Spectral Image Patch Matching
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DOI：10.1109/TGRS.2022.3176358.png)
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Abstract
En 中文
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 the two-branch network structure. Therefore, we propose a novel multibranch feature difference learning network (MFD-Net). Specifically, we build a multibranch 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 a 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.
Keywords:
Feature extraction
Data mining
Measurement
Task analysis
Robustness
Technological innovation
Representation learning
Combined metric network
cross-spectral image patch matching
feature difference
multibranch feature difference learning network (MFD-Net)
Journal
IF:
8.6
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2.1W
Citations:
10.7W