PolSAR Image Classification on San Francisco
99.39Accuracy (Bare Soil)WaveletCNN
Evaluation Results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| WaveletCNNTrain=1,604, CNN Category=Real-Valued CNN2026.04 | 99.39 | 92.61 | 95.34 | 96.83 | 75.75 | 94.53 | 91.98 | 91.51 | |
| PolSARFormerTrain=1,604, CNN Category=Real-Valued CNN2026.04 | 99.36 | 97.27 | 96.91 | 90.44 | 90.15 | 93.77 | 94.83 | 90.51 | |
| DDF2PolTrain=1,604, CNN Category=Dual-Domain CNN2026.04 | 99.36 | 98.09 | 95.79 | 96.22 | 94.47 | 96.12 | 96.79 | 94.03 | |
| 3D-CV-CNNTrain=1,604, CNN Category=Complex-Valued CNN2026.04 | 99.21 | 99.21 | 91.7 | 95.42 | 94.82 | 94.21 | 96.07 | 91.21 | |
| 3D-CNNTrain=1,604, CNN Category=Real-Valued CNN2026.04 | 98.6 | 93.74 | 95.12 | 90.3 | 82.99 | 92.2 | 92.15 | 88.16 | |
| HybridCVNetTrain=1,604, CNN Category=Complex-Valued CNN2026.04 | 97.58 | 99.27 | 95.15 | 96.23 | 94.83 | 95.96 | 96.61 | 93.79 | |
| CV 2D-3DTrain=1,604, CNN Category=Complex-Valued CNN2026.04 | 97.31 | 99.28 | 95.04 | 94.88 | 94.87 | 95.34 | 96.28 | 92.86 | |
| SDF2Net2024.02 | 79.98 | 94.49 | 98.7 | 98.94 | 87.42 | 97.13 | 91.31 | 95.5 | |
| Wavelet CNN2024.02 | 78.97 | 94.62 | 99.26 | 96.21 | 60.25 | 94.65 | 85.86 | 91.58 | |
| 3D-CVNN2024.02 | 73.13 | 96.31 | 99.24 | 95.52 | 86.44 | 96.19 | 90.33 | 94.07 | |
| CV-CNN-SE2024.02 | 57.81 | 94.82 | 99.11 | 98.47 | 77.71 | 96.37 | 85.58 | 94.28 | |
| 2D-CVNN2024.02 | 47.49 | 91.27 | 99.37 | 97.78 | 78.87 | 95.8 | 82.95 | 93.38 | |
| SVM2024.02 | 0.04 | 40.61 | 98.37 | 95.65 | 64.21 | 88.73 | 59.77 | 81.75 |