Author Affiliations
1School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China2Shaanxi Provincial Institute of Cultural Relics Protection, Xi'an 710075, Shaanxi, Chinashow less
Fig. 1. Schematic diagrams of 2D-CNN and 3D-CNN. (a) 2D-CNN; (b) 3D-CNN
Fig. 2. Residual learning
Fig. 3. Schematic diagrams of atrous convolution kernel. (a) r=1; (b) r=2; (c) r=3
Fig. 4. Sserial hole convolution module
Fig. 5. Multiscale feature fusion module
Fig. 6. Schematic of MFAC-Res3D-CNN
Fig. 7. Multispectral image acquisition system of murals
Fig. 8. Multispectral images of murals in each band
Fig. 9. Simulated mural. (a) Simulated mural image; (b) pseudo color image; (c) truth image
Fig. 10. Comparison of different network classification results. (a) Truth image; (b) SVM; (c) 2D-CNN; (d) Res-3D-CNN; (e) MFAC-Res3D-CNN
Fig. 11. Comparison of different network classification details. (a) Truth image; (b) SVM;(c) 2D-CNN; (d) Res-3D-CNN; (e) MFAC-Res3D-CNN
Number | Category | Color | Number of samples |
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0 | Background | | 871622 | 1 | Mercuric sulfide | | 57778 | 2 | Mineral green | | 49641 | 3 | Chrome yellow | | 294816 | 4 | Graphite | | 58765 | 5 | Lazurite | | 77061 | 6 | Minium | | 1417 |
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Table 1. Sample of multispectral image dataset of simulated murals
Category | Background | Mercuric sulfide | Mineral green | Chrome yellow | Graphite | Lazurite | Minium |
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Background | 865044 | 1036 | 1677 | 2548 | 1082 | 215 | 20 | Mercuric sulfide | 573 | 57166 | 22 | 17 | 0 | 0 | 0 | Mineral green | 1677 | 100 | 47859 | 5 | 0 | 0 | 0 | Chrome yellow | 2692 | 91 | 1 | 292031 | 0 | 1 | 0 | Graphite | 2049 | 0 | 7 | 136 | 56573 | 0 | 0 | Lazurite | 1452 | 0 | 22 | 52 | 18 | 75517 | 0 | Minium | 135 | 0 | 0 | 1 | 0 | 0 | 1281 |
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Table 2. Classification confusion matrix of article model
Category | SVM | 2D-CNN | Res-3D-CNN | MFAC-Res3D-CNN |
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Background | 84.36 | 97.49 | 97.34 | 99.24 | Mercuric sulfide | 92.36 | 91.05 | 95.43 | 98.94 | Mineral green | 78.53 | 92.34 | 94.25 | 96.41 | Chrome yellow | 88.53 | 99.32 | 97.50 | 99.05 | Graphite | 80.31 | 99.88 | 94.64 | 96.26 | Lazurite | 86.72 | 84.90 | 97.01 | 97.99 | Minium | 63.78 | 82.63 | 89.87 | 90.40 | OA | 84.72 | 91.45 | 97.57 | 98.87 | AA | 82.08 | 92.51 | 95.14 | 96.89 | Kappa | 78.60 | 89.98 | 95.41 | 98.04 |
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Table 3. Comparison of dataset classification accuracy results
Time | SVM | 2D-CNN | Res-3D-CNN | MFAC-Res3D-CNN |
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Train | 678.5 | 1037.8 | 1265.6 | 1301.5 | Test | 7.98 | 10.75 | 15.95 | 17.05 |
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Table 4. Comparison of training and testing time of different algorithms