Multimodal Remote Sensing Classification on Yellow River Estuary
79.55Overall Accuracy (OA)BDGF
Evaluation results
| Method | Links | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| BDGFNumber of training samples per class=1002025.09 | 79.55 | 88.23 | 76.72 | 81 | 75.57 | 79.54 | 80.21 | 71 | — | |
| MSFMambaNumber of training samples per class=1002025.09 | 78.78 | 88.74 | 72.28 | 88.88 | 77.55 | 80.28 | 81.55 | 70.63 | — | |
| HLMambaNumber of training samples per class=1002025.09 | 77.99 | 86.2 | 71.37 | 88.84 | 82.02 | 78.49 | 81.38 | 69.63 | — | |
| NCGLFNumber of training samples per class=1002025.09 | 77.97 | 90.63 | 71.03 | 88.98 | 76.29 | 76.19 | 80.62 | 69.6 | — | |
| SS-MAENumber of training samples per class=1002025.09 | 76.81 | 90.48 | 69.11 | 80.72 | 78.8 | 84.98 | 80.82 | 68.13 | — | |
| AsyFFNetNumber of training samples per class=1002025.09 | 76.45 | 91.54 | 69.91 | 75.24 | 79.78 | 83.12 | 79.92 | 67.5 | — | |
| CALCNumber of training samples per class=1002025.09 | 75.84 | 86.14 | 73.48 | 64.48 | 71.87 | 89.36 | 77.07 | 66.17 | — | |
| Fusion-HCTNumber of training samples per class=1002025.09 | 75.7 | 91.55 | 67.55 | 83.97 | 77.35 | 76.45 | 79.37 | 66.7 | — | |
| MACNNumber of training samples per class=1002025.09 | 75.18 | 92.34 | 65.78 | 86.25 | 79.65 | 74.01 | 79.6 | 66.08 | — | |
| UACLNumber of training samples per class=1002025.09 | 72.08 | 91.92 | 64.07 | 71.67 | 77 | 80.85 | 76.9 | 62.16 | — | |
| S2FinArea Description=Wetlands, Shangdong, Modalities=HSI + SAR, Channels=166 + 4, Spatial Size=960 × 1170, Classes=5, Numbers=4646712025.10 | 67.54 | — | — | — | — | — | — | — | 2.2 | |
| Top-BaselineArea Description=Wetlands, Shangdong, Modalities=HSI + SAR, Channels=166 + 4, Spatial Size=960 × 1170, Classes=5, Numbers=4646712025.10 | 65.34 | — | — | — | — | — | — | — | — |