Yu-Ze LI, Yan ZHANG, Yu CHEN, Chun-Ling YANG. An unsupervised few-shot infrared aerial object recognition network based on deep-shallow learning graph model[J]. Journal of Infrared and Millimeter Waves, 2023, 42(6): 916
- Journal of Infrared and Millimeter Waves
- Vol. 42, Issue 6, 916 (2023)
Fig. 1. The framework of aerial object recognition model
Fig. 2. Framework of domain invariant deep feature extraction module
Fig. 3. Histograms of the deep features in source and target domains，（a）the sample in the source domain，（b）the histogram of the deep features in the source domain，（c）the sample in the target domain，（d）the histogram of the deep features in the target domain
Fig. 4. Bar charts of shallow features，（a）the sample，（b）SIFT，（c）LBP，（d）Harris，（e）HOG，（f）grayscale histogram
Fig. 5. Framework of GCN-based feature fusion module
Fig. 6. Histograms of the fused features in source and target domains，（a）the sample in source domain，（b）the histogram of the fused features in source domain，（c）the sample in target domain，（d）the histogram of the fused features in target domain
Fig. 7. Samples of typical aerial object images
Fig. 8. Experimental results on the sensitivity of hgperparameter λwd
Fig. 9. Ressults of feature visualization for D-SLGM algorithm，（a） visualization of the deep features extracted by baseline CNN，（b）visualization of the domain invariant deep features extracted by D-SLGM，（c）visualization of the final features extracted by D-SLGM
| ||||||||||||||||||||||||||||||||||||||
Table 1. Accuracies of different algorithms on aerial objects datasets (%)
| ||||||||||||||||||||||||||||
Table 2. Results of ablation experiments on D-SLGM (%)
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20