Table 2 Classification results
From: Potential use of hyperspectral data to classify forest tree species
Parameter | Classification | |||||||
|---|---|---|---|---|---|---|---|---|
All 129 bands | 36 chosen bands | First three Principal component analysis bands | First seven minimum noise fraction bands | |||||
Algorithm | Accuracy (%) | Kappa | Accuracy (%) | Kappa | Accuracy (%) | Kappa | Accuracy (%) | Kappa |
Parallelepiped | 5.3 | 0.005 | 15.3 | 0.05 | 30.3 | 0.23 | 10.3 | 0.06 |
Minimum Distance | 62.7 | 0.56 | 40.3 | 0.3 | 61.7 | 0.55 | 84.7 | 0.82 |
Mahalanobis distance | NETP | NETP | NETP | NETP | 72.7 | 0.68 | 81.3 | 0.78 |
Maximum likelihood | NETP | NETP | NETP | NETP | 88.3 | 0.86 | 90.7 | 0.89 |
Spectral angle mapping | 75 | 0.7 | 39.3 | 0.3 | 69.3 | 0.64 | 85 | 0.82 |
Spectral information divergence | 64.7 | 0.58 | 38.3 | 0.28 | 37 | 0.27 | 81.3 | 0.78 |
Binary encoding | 31 | 0.22 | 33.6 | 0.23 | 11.7 | 0.05 | 44 | 0.37 |
Neural networks | 6.7 | 0.0004 | 66.3 | 0.6 | 68.3 | 0.62 | 63.7 | 0.56 |
Support vector machine | 76.7 | 0.72 | 58.7 | 0.5 | 61 | 0.53 | 72.7 | 0.68 |