Research Article
Optimization Strategy of a Stacked Autoencoder and Deep Belief Network in a Hyperspectral Remote-Sensing Image Classification Model
Table 4
Confusion matrix of DBN-SR classification accuracy for ROSIS-3 data.
| | Asphalt | Bare ground | Gravel | Grassland | Metal sheet | Brick | Shadows | Trees | Total | Accuracy (%) |
| Asphalt | 426 | 19 | 31 | 0 | 0 | 26 | 0 | 0 | 502 | 85.53 | Bare ground | 0 | 275 | 45 | 0 | 0 | 15 | 0 | 0 | 335 | 81.82 | Gravel | 45 | 31 | 375 | 0 | 0 | 35 | 0 | 0 | 458 | 84.05 | Grassland | 0 | 0 | 0 | 321 | 0 | 0 | 42 | 22 | 385 | 94.01 | Metal sheet | 0 | 0 | 0 | 0 | 710 | 52 | 0 | 0 | 762 | 96.31 | Brick | 21 | 26 | 9 | 0 | 0 | 333 | 0 | 0 | 389 | 86.98 | Shadows | 11 | 0 | 0 | 0 | 0 | 0 | 698 | 50 | 759 | 93.95 | Trees | 0 | 0 | 0 | 0 | 0 | 0 | 31 | 479 | 510 | 95.33 | Total | 503 | 351 | 460 | 321 | 710 | 461 | 771 | 551 | | | Accuracy (%) | 84.69 | 78.35 | 81.52 | 100 | 100 | 72.23 | 90.53 | 86.90 | | |
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Overall accuracy = 3617/4100 = 88.22%; kappa coefficient = (0.8822 − 0.1363)/(1 − 0.1363) = 0.87.
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