Scene Classification on AID TR=50%
98.6AccuracyFENet
Evaluation Results
| Method | Links | |
|---|---|---|
| FENet#P=23.9M, FLOPS=92.0G2024.03 | 98.6 | |
| MGML-FENetPretrain=IMP, Backbone=DenseNet-121, Method=MGML-FENet [77]2022.08 | 98.6 | |
| ViT-BPretrain=MAE, Backbone=ViT-B, Method=—2022.08 | 98.56 | |
| MBENet#P=23.9M, FLOPS=108.5G2024.03 | 98.54 | |
| SelectiveMAEBackbone=ViT-L [2], Params (M)=307, Throughput/Minute=533k2024.06 | 98.52 | |
| RVSABackbone=VIT-B+RVSA [21], Params (M)=862024.06 | 98.5 | |
| SelectiveMAEBackbone=ViT-L [2], Params (M)=307, Throughput/Minute=533k, Pre-training=OpticalRS-13 (4M)2024.06 | 98.5 | |
| RVSA#P=114.4M, FLOPS=301.3G2024.03 | 98.5 | |
| ViTAE-B + RVSA♦Pretrain=MAE, Backbone=ViTAE-B + RVSA*, Method=—2022.08 | 98.5 | |
| ViTAE-B + RVSAPretrain=MAE, Backbone=ViTAE-B + RVSA, Method=—2022.08 | 98.48 | |
| ViT-B + RVSA♦Pretrain=MAE, Backbone=ViT-B + RVSA*, Method=—2022.08 | 98.44 | |
| ViTAE-BPretrain=MAE, Backbone=ViTAE-B, Method=—2022.08 | 98.42 | |
| RingMoPretrain=RingMo [32], Backbone=ViT-B, Method=—2022.08 | 98.38 | |
| RingMoBackbone=Swin-B [102], Params (M)=882024.06 | 98.34 | |
| RingMoPretrain=RingMo [32], Backbone=Swin-B, Method=—2022.08 | 98.34 | |
| ViTAE-B + VSAPretrain=MAE, Backbone=ViTAE-B + VSA, Method=—2022.08 | 98.34 | |
| ViT-B + RVSAPretrain=MAE, Backbone=ViT-B + RVSA, Method=—2022.08 | 98.33 | |
| RSP-Swin#P=27.5M, FLOPS=37.7G2024.03 | 98.3 | |
| ViT-B + VSAPretrain=MAE, Backbone=ViT-B + VSA, Method=—2022.08 | 98.3 | |
| SelectiveMAEBackbone=ViT-B [2], Params (M)=86, Throughput/Minute=556k2024.06 | 98.28 | |
| RSP-VITAE#P=19.3M, FLOPS=119.1G2024.03 | 98.22 | |
| LSKNet-S#P=14.4M, FLOPS=54.4G2024.03 | 98.22 | |
| VITAEv2-SPretrain=RSP, Backbone=VITAEv2-S, Method=—2022.08 | 98.22 | |
| LSKNet-T#P=4.3M, FLOPS=19.2G2024.03 | 98.14 | |
| SelectiveMAEBackbone=ViT-B [2], Params (M)=86, Throughput/Minute=556k, Pre-training=OpticalRS-13 (4M)2024.06 | 98.12 | |
| Swin-TPretrain=IMP, Backbone=Swin-T, Method=—2022.08 | 98.1 | |
| VITAEv2-SPretrain=IMP, Backbone=VITAEv2-S, Method=—2022.08 | 98.08 | |
| MAEBackbone=ViT-B [2], Params (M)=86, Throughput/Minute=264k, Pre-training=OpticalRS-13 (4M)2024.06 | 98.02 | |
| RSP-R50#P=25.6M, FLOPS=86.3G2024.03 | 97.89 | |
| CTNet2024.03 | 97.7 | |
| ScaleMAEBackbone=ViT-L [2], Params (M)=307, Throughput/Minute=206k2024.06 | 97.58 | |
| FSCNet#P=28.8M, FLOPS=166.1G2024.03 | 97.51 | |
| AGOSType=CNN, Backbone=DenseNet-1212022.05 | 97.43 | |
| KFBNet2024.03 | 97.4 | |
| ConvNext#P=28.0M, FLOPS=93.7G2024.03 | 97.4 | |
| GRMANet#P=54.1M, FLOPS=171.4G2024.03 | 97.39 | |
| GRMANetPretrain=IMP, Backbone=ResNet-50, Method=GRMANet [73]2022.08 | 97.39 | |
| SatLasBackbone=Swin-B [102], Params (M)=88, Throughput/Minute=243k2024.06 | 97.38 | |
| AGOSType=CNN, Backbone=ResNet-1012022.05 | 97.22 | |
| CADNetType=CNN, Year=20202022.05 | 97.16 | |
| MBLANet2024.03 | 97.14 | |
| MBLANetType=CNN, Year=20212022.05 | 97.14 | |
| TOVBackbone=ResNet-50 [101], Params (M)=262024.06 | 97.09 | |
| GFMBackbone=Swin-B [102], Params (M)=882024.06 | 97.09 | |
| EAM#P=>42.3M, FLOPS=>164.32024.03 | 97.06 | |
| UPetu#P=87.7M, FLOPS=>322.2G2024.03 | 97.06 | |
| EAMPretrain=IMP, Backbone=ResNet-101, Method=EAM [74]2022.08 | 97.06 | |
| AGOSType=CNN, Backbone=ResNet-502022.05 | 97.01 | |
| F2BRBM#P=25.6M, FLOPS=86.3G2024.03 | 96.97 | |
| F2BRBMPretrain=IMP, Backbone=ResNet-50, Method=F2BRBM [5]2022.08 | 96.97 | |
| IDCCP#P=25.6M, FLOPS=86.3G2024.03 | 96.95 | |
| SatMAEBackbone=ViT-L [2], Params (M)=307, Throughput/Minute=205k2024.06 | 96.94 | |
| MSDFFType=CNN, Year=20202022.05 | 96.74 | |
| Contourlet CNNType=CNN, Year=20212022.05 | 96.65 | |
| LSENet#P=25.9M, FLOPS=>86.3G2024.03 | 96.36 | |
| LSENetPretrain=IMP, Backbone=VGG-16, Method=LSENet [42]2022.08 | 96.36 | |
| LiGNetType=CNN, Year=20212022.05 | 96.19 | |
| MG-CAP#P=>42.3M, FLOPS=>164.3G2024.03 | 96.12 | |
| MG-CAPType=CNN, Year=20202022.05 | 96.12 | |
| SCCov#P=13.0M2024.03 | 96.1 | |
| ViT-B#P=86.0M, FLOPS=118.9G2024.03 | 96.08 | |
| ViT-BPretrain=IMP, Backbone=ViT-B, Method=—2022.08 | 96.08 | |
| MSANet#P=>42.3M, FLOPS=>164.32024.03 | 96.01 | |
| MSANetPretrain=IMP, Backbone=ResNet-101, Method=MSANet [75]2022.08 | 96.01 | |
| SeCoBackbone=ResNet-50 [101], Params (M)=262024.06 | 95.99 | |
| MA-FE#P=>25.6M, FLOPS=>86.3G2024.03 | 95.98 | |
| MF2NetType=CNN, Year=20202022.05 | 95.93 | |
| GASSLBackbone=ResNet-50 [101], Params (M)=262024.06 | 95.92 | |
| LSENetType=CNN, Year=20212022.05 | 95.82 | |
| DMSMILType=CNN, Year=20212022.05 | 95.65 | |
| CSPTPretrain=CSPT [80], Backbone=ViT-L, Method=—2022.08 | 95.62 | |
| GBNetType=CNN, Year=20202022.05 | 95.48 | |
| CSPTPretrain=CSPT [80], Backbone=ViT-B, Method=—2022.08 | 95.11 | |
| CACOBackbone=ResNet-50 [101], Params (M)=262024.06 | 95.05 | |
| MS2APType=CNN, Year=20212022.05 | 94.82 | |
| SSL4EOBackbone=ViT-S [2], Params (M)=222024.06 | 94.74 | |
| DSENetType=CNN, Year=20212022.05 | 94.5 | |
| MSCPType=CNN, Year=20182022.05 | 94.42 | |
| ASPPretrain=ASP [76], Backbone=ResNet-101, Method=—2022.08 | 94.2 | |
| RBFFPretrain=IMP, Backbone=MobileNet-V2 [78], Method=RBFF [79]2022.08 | 93.64 | |
| ARCNetType=RNN, Year=20182022.05 | 93.1 | |
| MIDCNetType=CNN, Year=20202022.05 | 92.53 | |
| RANetType=CNN, Year=20202022.05 | 92.35 | |
| APNetType=CNN, Year=20192022.05 | 92.15 | |
| SPPNetType=CNN, Year=2017, Note=Conducted by authors2022.05 | 91.45 | |
| TEXNetType=CNN, Year=20172022.05 | 90 | |
| VGGNet-16Type=CNN, Year=20172022.05 | 89.64 | |
| AlexNetType=CNN, Year=20172022.05 | 89.53 | |
| GoogLeNetType=CNN, Year=20172022.05 | 86.39 | |
| AGANType=GAN, Year=20192022.05 | 84.52 | |
| MARTAType=GAN, Year=20172022.05 | 81.57 | |
| LDA(SIFT)Type=Hand-crafted, Year=20172022.05 | 68.96 | |
| BOVW(SIFT)Type=Hand-crafted, Year=20172022.05 | 68.37 | |
| PLSA(SIFT)Type=Hand-crafted, Year=20172022.05 | 63.07 |