Multimodal Remote Sensing Classification on Berlin 100 samples per class (train)
89.87Class 1 AccuracyMSFMamba
Evaluation results
| Method | Links | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MSFMambaTraining samples per class=1002025.09 | 89.87 | 68.73 | 67.52 | 84.04 | 96.66 | 83.18 | 60.04 | 85.48 | 74.36 | 79.44 | 64.29 | |
| NCGLFTraining samples per class=1002025.09 | 89.86 | 67.63 | 67.65 | 85.69 | 95.27 | 86.69 | 62.16 | 94.79 | 74.24 | 81.22 | 64.55 | |
| HLMambaTraining samples per class=1002025.09 | 89.66 | 66.93 | 67.13 | 80.8 | 92.81 | 80.83 | 54.02 | 92.79 | 72.44 | 78.12 | 61.84 | |
| UACLTraining samples per class=1002025.09 | 88.93 | 72.06 | 66.99 | 82.55 | 88.96 | 53 | 21.66 | 95.05 | 72.9 | 61.26 | 71.15 | |
| SS-MAETraining samples per class=1002025.09 | 88.14 | 67.46 | 68.08 | 87.47 | 93.04 | 83.88 | 60.98 | 93.39 | 73.93 | 80.31 | 63.76 | |
| MACNTraining samples per class=1002025.09 | 84.96 | 65.84 | 64.94 | 86.72 | 94.37 | 85.02 | 52.93 | 80.14 | 71.85 | 76.86 | 61.16 | |
| BDGFTraining samples per class=1002025.09 | 82.34 | 70.87 | 76.81 | 85.97 | 96.12 | 74.95 | 57.95 | 94.49 | 75.11 | 79.94 | 64.73 | |
| CALCTraining samples per class=1002025.09 | 81.65 | 71.16 | 64.68 | 84.25 | 94.01 | 84.97 | 51.31 | 92.82 | 74.29 | 78.11 | 63.72 | |
| AsyFFNetTraining samples per class=1002025.09 | 81.29 | 69.62 | 62.05 | 82.04 | 93.06 | 74.09 | 60.12 | 91.19 | 73.07 | 76.68 | 61.83 | |
| Fusion-HCTTraining samples per class=1002025.09 | 79.67 | 68.04 | 64.73 | 89.51 | 96.09 | 80.33 | 57.2 | 92.44 | 73.18 | 78.5 | 62.64 |