Hyperspectral Image Classification on Indian Pines 10% (train)
98.76Overall AccuracySpectralNET
Evaluation results
| Method | Links | |||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SpectralNETTraining Samples=10%2021.04 | 98.76 | 98.59 | 98.61 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| HybridSNTraining Samples=10%2021.04 | 98.39 | 98.16 | 98.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| FuSENetTraining Samples=10%2021.04 | 97.11 | 97.25 | 97.32 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| LGESTTraining samples=10%2026.03 | 93.69 | 92.8 | 80.72 | 86.46 | 92.51 | 91.25 | 94.24 | 93.43 | 93.76 | 35.85 | 99.75 | 3.78 | 87.97 | 96.05 | 90.22 | 95.7 | 98.3 | 95.13 | 57.39 | |
| GSC-ViTTraining samples=10%2026.03 | 93.03 | 92.03 | 75.34 | 63.3 | 92.47 | 88.78 | 91.98 | 94.13 | 94.14 | 12.97 | 99.2 | 0 | 86.98 | 96.25 | 86.41 | 95.33 | 98.46 | 95.48 | 24.92 | |
| MorphformerTraining samples=10%2026.03 | 91.71 | 90.52 | 73.37 | 42.62 | 90.83 | 87.99 | 91.78 | 91.53 | 92.38 | 19.05 | 99.06 | 0 | 86.36 | 94.79 | 86.36 | 90.39 | 97.27 | 93.23 | 35.77 | |
| 2D CNNTraining samples=10%2026.03 | 91.09 | 89.8 | 70.12 | 15.56 | 88.73 | 87.7 | 91.35 | 92.15 | 93.22 | 0 | 99.88 | 0 | 81.88 | 95.13 | 85.03 | 94.53 | 97.71 | 91.98 | 25.39 | |
| SS-TMNetTraining samples=10%2026.03 | 89.77 | 88.28 | 71.48 | 68.73 | 87.35 | 87.7 | 89.03 | 87.31 | 92.49 | 0 | 98.74 | 0 | 81.95 | 93.12 | 82.35 | 96.16 | 95.34 | 93.61 | 11.01 | |
| HiTTraining samples=10%2026.03 | 88.77 | 87.13 | 68.08 | 14.19 | 86.47 | 87.36 | 86.87 | 82.94 | 90.63 | 0 | 97.18 | 0 | 83.13 | 92.09 | 82.69 | 94.93 | 94.57 | 90.27 | 22.73 | |
| SyCNNTraining samples=10%2026.03 | 88.14 | 86.45 | 73.13 | 73.79 | 84.13 | 85.64 | 91.3 | 88.75 | 90.52 | 0 | 98.24 | 0 | 79.72 | 90.99 | 85.02 | 90.08 | 93.68 | 86.99 | 58.16 | |
| 3D-CNNTraining Samples=10%2021.04 | 82.62 | 79.25 | 76.51 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SVMTraining Samples=10%2021.04 | 81.67 | 78.76 | 79.84 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| M3D-CNNTraining Samples=10%2021.04 | 81.39 | 81.2 | 75.22 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 2D-CNNTraining Samples=10%2021.04 | 80.27 | 78.26 | 68.32 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Deep ViTTraining samples=10%2026.03 | 79.48 | 76.25 | 60.31 | 7.95 | 78.18 | 71.36 | 87.78 | 57.34 | 82.43 | 0 | 98.64 | 0 | 65.91 | 81.24 | 68.99 | 87.16 | 91.47 | 89.07 | 32.34 | |
| CaiTTraining samples=10%2026.03 | 75.76 | 71.87 | 55.53 | 2.73 | 75.02 | 70.62 | 86.5 | 49.31 | 73.46 | 0 | 97.45 | 0 | 52.4 | 79.35 | 69.29 | 85 | 89.82 | 74.28 | 0 | |
| 3D CNNTraining samples=10%2026.03 | 74.38 | 70.13 | 48.94 | 0 | 64.04 | 66.25 | 14.33 | 83.09 | 85.85 | 0 | 99.15 | 0 | 69.05 | 77.73 | 58.62 | 44.36 | 92.18 | 58.58 | 0 | |
| HybridSNTraining samples=10%2026.03 | 65.64 | 59.6 | 40.83 | 0 | 60.05 | 45.77 | 24.53 | 38.52 | 62.89 | 0 | 80.74 | 0 | 43.36 | 79.81 | 43.26 | 38.22 | 82.45 | 36.13 | 0 | |
| Cross ViTTraining samples=10%2026.03 | 64.88 | 58.8 | 44.38 | 0 | 60.89 | 26.65 | 61.66 | 56.83 | 73.54 | 0 | 95.82 | 0 | 46.57 | 69.06 | 37.54 | 43.71 | 87.17 | 68.63 | 4.7 | |
| ViTTraining samples=10%2026.03 | 51.93 | 42.3 | 30.96 | 0 | 48.68 | 10.76 | 36.88 | 13.84 | 58.41 | 0 | 94.21 | 0 | 6.32 | 57.9 | 8.9 | 38.77 | 81.65 | 10.56 | 0 | |
| SVMTraining samples=10%2026.03 | 50.81 | 40.1 | 28.03 | 0 | 36.47 | 0 | 0 | 0.23 | 66.95 | 0 | 87.92 | 0 | 0 | 57.36 | 0 | 0 | 82.83 | 0 | 44.68 |