Acta Photonica Sinica, Volume. 51, Issue 4, 0430002(2022)
Pigment Classification Method of Mural Sparse Multi-spectral Image Based on Space Spectrum Joint Feature
Figures & Tables(17)
Fig. 6. The training results of different methods under the paint board
Fig. 8. Classification results of different methods under self-made murals
Fig. 10. 16-channel multispectral image of Venerable Injanta's skirt
Fig. 11. Partial region samples and classification results of skirts
Table 1. The division of training set and test set of multispectral paint board
View tableView in ArticleTable 1. The division of training set and test set of multispectral paint board
Class number Class name Training Test Total 3 496 31 383 1 Chrome yellow 246 2 214 2 Orpiment 248 2 232 3 Garcinia 257 2 313 4 Head green 251 2 259 5 Four green 249 2 160 6 Head cyan 255 2 295 7 Cerulean 261 2 349 8 Four cyan 264 2 376 9 Lazurite 236 2 124 10 Crimson 251 2 259 11 Scarlet 249 2 241 12 Cinnabar 243 2 187 13 Ocher 247 2 223 14 Vermilion 239 2 151
Table 2. Classification accuracy of different methods under pigment board
View tableView in ArticleTable 2. Classification accuracy of different methods under pigment board
Class LSTM CNN SSJF Chrome yellow 97.88 99.10 99.77 Orpiment 97.46 98.21 98.17 Garcinia 96.45 98.27 97.97 Head green 96.37 99.34 99.56 Four green 98.52 99.63 99.82 Head cyan 96.27 98.57 99.26 Cerulean 98.64 99.58 98.26 Four cyan 94.82 96.93 97.42 Lazurite 99.85 99.87 99.95 Crimson 94.57 99.25 99.94 Scarlet 93.59 97.24 97.42 Cinnabar 99.22 99.75 99.95 Ocher 98.25 99.37 99.82 Vermilion 99.81 99.58 99.81 OA/% 97.28 98.94 98.99 Kappa×100 97.07 98.86 98.91
Table 3. Comparison of the scales of subjective image evaluation
View tableView in ArticleTable 3. Comparison of the scales of subjective image evaluation
Level Absolute measurement scale Relative measurement scale 1 Very good The best in the group 2 Better Better than the average in the group 3 Generally Average in the group 4 Poor Worse than the average in the group 5 Very bad Worst in the group
Table 4. Evaluation results of dual stimulation injury classification method
View tableView in ArticleTable 4. Evaluation results of dual stimulation injury classification method
Algorithm Result MDC Very bad SID Poor SAM Generally SVM Generally MSCNN Better LSTM Better SSJF Very good
Table 5. Objective evaluation results of image quality
View tableView in ArticleTable 5. Objective evaluation results of image quality
Algorithm Evaluation index RMSE PSNR SSIM MDC 39.84 16.12 0.768 8 SID 28.11 19.15 0.887 1 SAM 22.49 19.15 0.894 4 SVM 30.93 18.32 0.864 2 MSCNN 27.81 19.25 0.833 9 LSTM 19.82 22.19 0.880 2 SSJF 2.84 39.06 0.987 4
Table 6. Classification accuracy of self-made simulated murals by different methods
View tableView in ArticleTable 6. Classification accuracy of self-made simulated murals by different methods
Class MDC SID SAM SVM LSTM CNN SSJF Vermilion 92.23 93.36 95.96 99.38 99.36 99.38 99.47 Chrome yellow 91.37 99.92 99.91 98.97 99.86 99.94 99.97 Four green 93.30 93.24 99.61 98.87 97.63 92.88 100.00 Three green 88.53 98.70 99.85 73.42 91.70 93.89 99.92 Lazurite 82.50 94.64 90.01 98.28 99.23 99.63 99.97 Head green 40.95 66.38 70.34 97.59 95.99 98.18 99.78 OA/% 69.36 97.75 97.54 98.41 98.55 98.68 99.97 Kappa×100 46.92 94.45 93.96 97.47 97.52 97.74 99.95
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Daoquan WEI, Huiqin WANG, Ke WANG, Zhan WANG, Gang ZHEN. Pigment Classification Method of Mural Sparse Multi-spectral Image Based on Space Spectrum Joint Feature[J]. Acta Photonica Sinica, 2022, 51(4): 0430002
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Received: Sep. 14, 2021
Accepted: Dec. 26, 2021
Published Online: May. 18, 2022
The Author Email: WANG Huiqin (hqwang@xauat.edu.cn)
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