Hyperspectral Image Classification on Indian Pines
96.63Overall Accuracy (OA)two-stage pixel-wise label propagation scheme
Evaluation results
| Method | Links | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| two-stage pixel-wise label propagation schemeTraining Examples per Class=5, PCA Dimensions=302026.01 | 96.63 | 91.76 | 0.9616 | — | 97.78 | 94.06 | 90.42 | 97.44 | 95.61 | 100 | 100 | 99.36 | 100 | 100 | 94.96 | 99.15 | 99.01 | 100 | 100 | 100 | — | |
| DSSPLTraining Examples per Class=5, PCA Dimensions=302026.01 | 85.91 | 87.02 | 0.8332 | — | 99.13 | 70.78 | 78.29 | 85.4 | 81.7 | 89.4 | 98.21 | 99.87 | 99 | 81.48 | 82.9 | 72.48 | 99.51 | 87.12 | 88.06 | 99.03 | — | |
| FSSTraining Examples per Class=5, PCA Dimensions=302026.01 | 81.42 | 78.83 | 0.8028 | — | 77.15 | 83.88 | 77.59 | 79.65 | 69.07 | 81.38 | 72.64 | 91.29 | 66.1 | 75.08 | 90.42 | 79.21 | 88.55 | 74.83 | 64.8 | 89.63 | — | |
| STSE-DWLRTraining Examples per Class=5, PCA Dimensions=302026.01 | 80.52 | 85.76 | 0.7868 | — | 98.48 | 47.91 | 64.25 | 83.5 | 80.25 | 91.16 | 97.86 | 99.94 | 99 | 69.89 | 83.29 | 77.64 | 99.51 | 83.04 | 87.28 | 95.14 | — | |
| SGLTraining Examples per Class=5, PCA Dimensions=302026.01 | 72.7 | 83 | 0.694 | — | 96.52 | 48.56 | 57.49 | 83.84 | 81.1 | 83.01 | 96.79 | 99.69 | 100 | 81.1 | 66.1 | 68.57 | 99.51 | 86.03 | 81.66 | 98.06 | — | |
| PLTraining Examples per Class=5, PCA Dimensions=302026.01 | 70.6 | 80.33 | 0.6592 | — | 91.3 | 48.63 | 45.92 | 84.68 | 79.79 | 81.92 | 94.29 | 99.73 | 86.47 | 72.67 | 64.75 | 61.25 | 99.06 | 82.83 | 79.02 | 98.92 | — | |
| GFHFTraining Examples per Class=5, PCA Dimensions=302026.01 | 64.99 | 70.57 | 0.6641 | — | 86.09 | 55.64 | 51.41 | 74.6 | 77.14 | 70.11 | 97.14 | 99.2 | 95.25 | 72.13 | 63.6 | 64.4 | 89.51 | 73.24 | 75.54 | 89.03 | — | |
| CNN-PPFTraining Examples per Class=5, PCA Dimensions=302026.01 | 30.87 | 41.44 | 0.2488 | — | 66.1 | 8.06 | 8.56 | 57.46 | 7.2 | 22.65 | 44.35 | 54.76 | 58 | 30.65 | 26.21 | 9.54 | 60.1 | 76.06 | 43.28 | 90 | — | |
| KANetinput size=17 x 17 x 200, type=base2025.04 | 0.9994 | 0.9977 | 0.9993 | — | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.9986 | 1 | 1 | 1 | 1 | 0.9643 | — | |
| LGCNet2025.04 | 0.9985 | 0.9953 | 0.9983 | — | 1 | 0.9992 | 0.9987 | 1 | 1 | 0.9956 | 0.9583 | 1 | 1 | 0.9978 | 0.9982 | 1 | 1 | 1 | 1 | 0.9773 | — | |
| MVNet2025.07 | 0.9974 | 0.9967 | 99.7 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SSRN2025.07 | 0.9962 | 0.9346 | 99.57 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SSRN2025.04 | 0.9962 | 0.9346 | 0.9957 | — | 1 | 0.9985 | 0.9983 | 1 | 0.9978 | 0.9981 | 1 | 1 | 0 | 1 | 0.9962 | 0.9917 | 1 | 0.9887 | 1 | 0.9851 | — | |
| DGCNet2025.07 | 0.9958 | 0.9955 | 99.53 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| DGCNet2025.04 | 0.9958 | 0.9955 | 0.9953 | — | 1 | 0.9947 | 0.9951 | 0.9765 | 1 | 0.9988 | 1 | 1 | 1 | 0.9885 | 0.9972 | 0.9956 | 1 | 0.9987 | 1 | 0.983 | — | |
| CNN-MRF10% training samples=true, Data augmentation=true, Time=1454.622017.05 | 0.9932 | 0.9927 | 0.9921 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 3D-SE-DenseNet2025.07 | 0.9884 | 0.9842 | 98.6 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 3D-SE-DenseNet2025.04 | 0.9884 | 0.9842 | 0.986 | — | 0.9587 | 0.9882 | 0.9912 | 0.9483 | 0.9986 | 0.9933 | 0.9737 | 1 | 1 | 0.9948 | 0.9895 | 0.9575 | 0.9928 | 0.9955 | 0.987 | 0.9651 | — | |
| DC-CNN10% training samples=true, Data augmentation=true, Time=4860.572017.05 | 0.9876 | 0.985 | 0.9858 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| ConvNeXt-T2025.11 | 0.9872 | 0.9912 | 0.99 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| MiCoBackbone=ViT-g2024.06 | 0.985 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| HGFNet2026.03 | 0.9846 | 0.9716 | 0.9824 | — | 0.6739 | 0.9545 | 0.9964 | 1 | 0.9793 | 1 | 1 | 1 | 1 | 0.999 | 0.9825 | 0.9713 | 1 | 0.9968 | 1 | 1 | — | |
| RCNN2025.11 | 0.9833 | 0.9871 | 0.98 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 3D-CNN2025.07 | 0.9823 | 0.988 | 97.96 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 3D-CNN2025.04 | 0.9823 | 0.988 | 0.9796 | — | 0.9688 | 0.9802 | 0.9774 | 0.9689 | 0.9912 | 0.9941 | 0.8889 | 1 | 1 | 1 | 0.9933 | 0.9767 | 0.9964 | 0.9965 | 0.9634 | 0.9792 | — | |
| Absolute SOTA2024.06 | 0.98 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| ADGAN2025.11 | 0.9737 | 0.9459 | 0.97 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SF2026.03 | 0.9668 | 0.9506 | 0.9622 | — | 0.9565 | 0.951 | 0.9711 | 0.9407 | 0.9711 | 0.9781 | 0.8571 | 1 | 0.8 | 0.9074 | 0.9829 | 0.9596 | 0.9804 | 0.9842 | 0.9689 | 1 | — | |
| HViT2026.03 | 0.9666 | 0.965 | 0.9619 | — | 1 | 0.9258 | 0.9663 | 0.8559 | 0.9959 | 0.9863 | 1 | 1 | 0.9 | 0.9095 | 0.9878 | 0.963 | 1 | 0.9921 | 0.9793 | 0.9783 | — | |
| WFormer2026.03 | 0.9653 | 0.9527 | 0.9604 | — | 0.8696 | 0.9314 | 0.9831 | 0.9153 | 0.9876 | 0.9808 | 1 | 1 | 0.8 | 0.9074 | 0.9813 | 0.9596 | 1 | 0.9858 | 0.9637 | 0.9783 | — | |
| MetaFormer2025.11 | 0.9434 | 0.9331 | 0.94 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SQS2025.11 | 0.9402 | 0.973 | 0.94 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| LGESTF(G)=108.32, P(M)=32.992026.03 | 0.9369 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| HyperspectralMAE2025.05 | 0.9237 | 0.958 | 0.91 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Hit2025.07 | 0.9167 | 0.8785 | 88.12 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SPP-DCNN10% training samples=true, Data augmentation=true, Time=328.182017.05 | 0.916 | 0.9396 | 0.9043 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 3D-ConvSST2025.11 | 0.9095 | 0.8456 | 0.91 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SS-DCNN10% training samples=true, Data augmentation=true, Time=255.322017.05 | 0.9076 | 0.8552 | 0.8944 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| OmniVec22025.07 | 0.906 | 0.893 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| DSFormer2025.11 | 0.9058 | 0.7705 | 0.91 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| RPNet-RF2025.07 | 0.9023 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| RvTF(G)=21.11, P(M)=8.942026.03 | 0.9014 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| HyLITE2025.07 | 0.898 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| HyLITE2025.05 | 0.898 | 0.9469 | 0.88 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SyCNNF(G)=194.11, P(M)=45.812026.03 | 0.8814 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| TC-GAN2025.07 | 0.8747 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| MASCActive Learning=true, Number of Queries=2112026.02 | 0.844 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 15.5 | |
| WMamba2026.03 | 0.8373 | 0.7582 | 0.8139 | — | 0.6522 | 0.8151 | 0.7735 | 0.5085 | 0.8471 | 0.8795 | 0.5714 | 0.9958 | 0.6 | 0.7572 | 0.9129 | 0.6936 | 0.7647 | 0.9289 | 0.7358 | 0.6957 | — | |
| ImageBindBackbone=ViT-H2024.06 | 0.836 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| LENDActive Learning=true, Number of Queries=2112026.02 | 0.828 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 97.6 | |
| Spectralformer2025.07 | 0.8176 | 0.8781 | 79.19 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SpectralFormerMode=Patch2025.07 | 0.8176 | 0.8781 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Spectralformer2025.04 | 0.8176 | 0.8781 | 0.7919 | — | 0.7052 | 0.8189 | 0.913 | 0.9553 | 0.8551 | 0.9932 | 0.8181 | 0.7548 | 0.7376 | 0.9877 | 0.9317 | 0.7848 | 1 | 0.7949 | 1 | 1 | — | |
| LANDActive Learning=true, Number of Queries=2112026.02 | 0.795 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 19.6 | |
| SpectralFormer2025.05 | 0.7897 | 0.8539 | 0.76 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SpectralFormerMode=Pixel2025.07 | 0.7855 | 0.8468 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Meta-TransformerBackbone=ViT-L2024.06 | 0.781 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 2-D CNNdimension=2-D2025.05 | 0.7589 | 0.8664 | 0.72 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| CaiTF(G)=16.79, P(M)=163.662026.03 | 0.7576 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| miniGCN2025.05 | 0.7511 | 0.7803 | 0.71 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| SVM2025.05 | 0.7236 | 0.8316 | 0.68 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| ViT2025.07 | 0.7186 | 0.7897 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| ViT2025.05 | 0.7186 | 0.7897 | 0.68 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| RNN2025.05 | 0.7066 | 0.7637 | 0.66 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| 1-D CNNdimension=1-D2025.05 | 0.7043 | 0.796 | 0.66 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| RF2025.05 | 0.698 | 0.7678 | 0.65 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Meta-Transformer-B16FBackbone=ViT-B/162025.07 | 0.6762 | 0.7809 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| KNN2025.05 | 0.5917 | 0.639 | 0.54 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Mamba2026.03 | 0.5834 | 0.4106 | 0.5119 | — | 0.0435 | 0.423 | 0.1807 | 0.0339 | 0.3306 | 0.8658 | 0 | 0.9331 | 0 | 0.3354 | 0.8192 | 0.3131 | 0.4608 | 0.9242 | 0.3627 | 0.5435 | — | |
| BCSCPerformance=Best case2026.03 | — | — | — | 0.32 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| FCM2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 55.38 | |
| k-means2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 0.77 | |
| KNN2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 0.54 | |
| Ours-G2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 31.03 | |
| Ours-O2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 108.64 | |
| RF2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 28.99 | |
| SVM2026.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | 0.19 | |
| UBCSCPerformance=Best case2026.03 | — | — | — | 0.34 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |