Image Classification on SAT-4 (test)
99.9AccuracyDeepSat V2
Evaluation Results
| Method | Links | |
|---|---|---|
| DeepSat V22019.11 | 99.9 | |
| Triplet networks2019.11 | 99.76 | |
| SatCNNNormalization=Z-score2019.11 | 99.69 | |
| SatCNNNormalization=linear2019.11 | 99.55 | |
| D-DSML-CaffeNet2019.11 | 99.51 | |
| Contrastive loss2019.11 | 98.74 | |
| TradCNNNormalization=Z-score2019.11 | 98.43 | |
| DCNN2019.11 | 98.41 | |
| DeepSat2019.11 | 97.95 | |
| DeepSatNeurons per layer=50, Layers=22015.09 | 97.946 | |
| DeepSatNeurons per layer=50, Layers=32015.09 | 97.654 | |
| DeepSatNeurons per layer=100, Layers=22015.09 | 97.292 | |
| DeepSatNeurons per layer=20, Layers=22015.09 | 97.115 | |
| DeepSatNeurons per layer=10, Layers=32015.09 | 96.8 | |
| DeepSatNeurons per layer=10, Layers=22015.09 | 96.585 | |
| DeepSatNeurons per layer=100, Layers=32015.09 | 95.609 | |
| DeepSatNeurons per layer=20, Layers=32015.09 | 95.473 | |
| MLPNormalization=Z-score2019.11 | 94.76 | |
| CNN2019.11 | 86.83 | |
| DBN2019.11 | 81.78 | |
| SDAE2019.11 | 79.98 |