Scene Classification on NWPU-RESISC45 (20% Train, Test Split)
95.82Overall AccuracyViTAE-B
Evaluation Results
| Method | Links | |
|---|---|---|
| ViTAE-BPretrain=MAE, Backbone=ViTAE-B, Method=—2022.08 | 95.82 | |
| ViT-BPretrain=MAE, Backbone=ViT-B, Method=—2022.08 | 95.78 | |
| ViTAE-B + RVSAPretrain=MAE, Backbone=ViTAE-B + RVSA, Method=—2022.08 | 95.69 | |
| RingMoPretrain=RingMo [32], Backbone=Swin-B, Method=—2022.08 | 95.67 | |
| ViTAE-B + RVSA♦Pretrain=MAE, Backbone=ViTAE-B + RVSA*, Method=—2022.08 | 95.66 | |
| VITAEv2-SPretrain=RSP, Backbone=VITAEv2-S, Method=—2022.08 | 95.6 | |
| ViTAE-B + VSAPretrain=MAE, Backbone=ViTAE-B + VSA, Method=—2022.08 | 95.53 | |
| ViT-B + RVSAPretrain=MAE, Backbone=ViT-B + RVSA, Method=—2022.08 | 95.49 | |
| ViT-B + RVSA♦Pretrain=MAE, Backbone=ViT-B + RVSA*, Method=—2022.08 | 95.45 | |
| MGML-FENetPretrain=IMP, Backbone=DenseNet-121, Method=MGML-FENet [77]2022.08 | 95.39 | |
| RingMoPretrain=RingMo [32], Backbone=ViT-B, Method=—2022.08 | 95.35 | |
| VITAEv2-SPretrain=IMP, Backbone=VITAEv2-S, Method=—2022.08 | 95.29 | |
| ViT-B + VSAPretrain=MAE, Backbone=ViT-B + VSA, Method=—2022.08 | 95.29 | |
| AGOSBackbone=DenseNet-121, Type=CNN2022.05 | 94.91 | |
| F2BRBMPretrain=IMP, Backbone=ResNet-50, Method=F2BRBM [5]2022.08 | 94.87 | |
| GRMANetPretrain=IMP, Backbone=ResNet-50, Method=GRMANet [73]2022.08 | 94.72 | |
| Swin-TPretrain=IMP, Backbone=Swin-T, Method=—2022.08 | 94.7 | |
| AGOSBackbone=ResNet-101, Type=CNN2022.05 | 94.69 | |
| MBLANetType=CNN2022.05 | 94.66 | |
| CADNetType=CNN2022.05 | 94.58 | |
| EAMPretrain=IMP, Backbone=ResNet-101, Method=EAM [74]2022.08 | 94.29 | |
| AGOSBackbone=ResNet-50, Type=CNN2022.05 | 94.28 | |
| ViT-BPretrain=IMP, Backbone=ViT-B, Method=—2022.08 | 93.96 | |
| MSDFFType=CNN2022.05 | 93.55 | |
| MSANetPretrain=IMP, Backbone=ResNet-101, Method=MSANet [75]2022.08 | 93.52 | |
| LSENetPretrain=IMP, Backbone=VGG-16, Method=LSENet [42]2022.08 | 93.34 | |
| LiGNetType=CNN2022.05 | 93.25 | |
| LSENetType=CNN2022.05 | 93.14 | |
| DMSMILType=CNN2022.05 | 93.05 | |
| MG-CAPType=CNN2022.05 | 92.95 | |
| MF2NetType=CNN2022.05 | 92.73 | |
| DCNNType=CNN2022.05 | 91.89 | |
| MS2APType=CNN2022.05 | 90.98 | |
| Contourlet CNNType=CNN2022.05 | 89.57 | |
| MSCPType=CNN2022.05 | 88.93 | |
| RBFFPretrain=IMP, Backbone=MobileNet-V2 [78], Method=RBFF [79]2022.08 | 88.05 | |
| RANetType=CNN2022.05 | 87.63 | |
| MIDCNetType=CNN2022.05 | 87.32 | |
| SPPNetType=CNN2022.05 | 84.64 | |
| AlexNetType=CNN2022.05 | 79.85 | |
| VGGNet-16Type=CNN2022.05 | 79.79 | |
| GoogLeNetType=CNN2022.05 | 78.48 | |
| AGANType=GAN2022.05 | 77.99 | |
| MARTAType=GAN2022.05 | 75.03 | |
| BoVW(SIFT)Type=Hand-crafted2022.05 | 44.97 |