Table 4.
Feature wavelength classification model using ELM
| Preprocessing | Training set accuracy (%) | Test set accuracy (%) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| SPA | CARS | IVISSA | CARS-SPA | IVISSA-SPA | SPA | CARS | IVISSA | CARS-SPA | IVISSA-SPA | |
| Raw | 92.09 | 93.87 | 92.89 | 94.93 | 92.53 | 90.4 | 92.36 | 91.2 | 90.31 | 91.91 |
| D1st | 94.67 | 96.88 | 96.71 | 96.98 | 97.87 | 92.53 | 94.93 | 95.2 | 94.93 | 96 |
| D2nd | 95.2 | 98.13 | 97.51 | 94.84 | 97.51 | 94.76 | 97.6 | 96 | 93.87 | 96.53 |
| SG + D1st | 94.76 | 98.31 | 97.15 | 97.42 | 96 | 92.44 | 96.8 | 96.27 | 94.13 | 93.42 |
| SG + D2nd | 96.53 | 96.53 | 96.27 | 94.93 | 98.13 | 91.73 | 96 | 95.11 | 91.02 | 96 |
| SNV + detrend | 93.33 | 95.2 | 96.17 | 93.07 | 96.8 | 92.36 | 93.6 | 92.27 | 91.91 | 94.13 |