国家自然科学基金(61725105)
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Figure 1
Overall framework of 3D building reconstruction for SAR images based on deep neural network
Figure 2
Architecture of complex valued deep semantic segmentation network for target detection in SAR images
Figure 3
Complex valued convolution layer based on coupling calculation method
Figure 4
Pooling and up-pooling layer based on complex valued data learning
Figure 6
Two groups of SAR image simulation results of typical buildings in different azimuth angles
Figure 7
Point clouds and simulation images generated based on models
Figure 8
Structure of SAR point generation network (SARPGN)
Figure 9
Results of SARPGN on its initial test set
Figure 10
SAR images of (a) Suzhou and (b) Hong Kong
Figure 11
Results of building facade detection in SAR images based on CV-SegNet. (a) SAR images; (b) prediction tags; (c) detection results of fusion of SAR images and prediction tags
Figure 12
Comparison of results of building facade detectionin SAR images. (a) Original amplitude SAR images; protectłinebreak (b) extraction resultsof RV-SegNet; (c) extraction results of CV-SegNet.
Figure 13
Comparisons of semantic segmentation network performance of building facade detection in SAR images. protectłinebreak (a) Overall accuracy; (b) recall; (c) F1 value. The left pictures show the performance of RV-SegNet, and the right pictures show the performance of CV-SegNet. The orange curves in the figures are the results on training set, and the blue curves are the results on test set.
Figure 14
3D models of typical buildings in Hong Kong area. (a) Harbourfront landmark; (b) International Finance Centre; (c) Laguna City; (d) Hyatt Regency; (e) Empire Centre; (f) Hopewell Centre.
| Item | RV-SegNet | CV-SegNet |
| Overall accuracy | 0.996 | 0.998 |
| Recall | 0.900 | 0.920 |
| F1 value | 0.920 | 0.940 |
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