Image Classification on Indian Pines (test)
96.09Class 1 AccuracyBASS
Evaluation results
| Method | Links | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| BASS2016.12 | 96.09 | 98.25 | 100 | 99.24 | 100 | 94.82 | 94.41 | 97.46 | 99.9 | 96.77 | |
| PPFMethod Variant=Pixel-Pair Features2016.12 | 92.99 | 96.66 | 98.58 | 100 | 100 | 96.24 | 87.8 | 98.98 | 99.81 | 94.34 | |
| SVMFeature Selection=Random Feature Selection, Implementation=libsvm2016.12 | 88.73 | 91.2 | 97.52 | 99.86 | 100 | 91.67 | 78.79 | 93.76 | 98.74 | 89.83 | |
| ELM2016.12 | 86.06 | 88.19 | 96.07 | 99.73 | 100 | 90.02 | 71 | 95.62 | 98.66 | 87.33 | |
| CNNArchitecture=Hu et al.2016.12 | 78.58 | 85.23 | 95.75 | 99.81 | 99.64 | 89.63 | 81.55 | 95.42 | 98.59 | 86.44 | |
| MLPArchitecture=Ne-150-100-50-C2016.12 | 77.77 | 79.05 | 94.7 | 98.11 | 99.64 | 83.68 | 79.6 | 89.31 | 98.12 | 85.48 | |
| k-NNImplementation=scikit learn2016.12 | 61.83 | 72.65 | 95.65 | 98.9 | 100 | 80.76 | 59.39 | 75.72 | 94.86 | 76.24 |