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Hyperspectral Image Classification on Pavia University (5% samples per class)

99.92Overall AccuracySCT-Net
82.073686.706891.3495.9732May 1, 2017Oct 30, 2018Apr 29, 2020Oct 28, 2021Apr 28, 2023Oct 26, 2024Apr 26, 2026

Evaluation results

MethodLinks
SCT-NetTraining samples ratio=5%2026.0499.9299.8899.8999.8710099.8599.6910010010099.6999.89
CNN-MRFTime=1156.432017.0599.7199.5599.63—————————
SSFTTTraining samples ratio=5%2026.0499.799.499.6199.9499.9798.9798.8199.899.9699.9299.1498.05
DC-CNNTime=3421.252017.0599.6899.599.58—————————
MASSFormerTraining samples ratio=5%2026.0499.4899.0699.3199.3799.9996.9198.7610099.8599.9298.5298.66
HybridSNTraining samples ratio=5%2026.0498.1697.3597.5796.5194.4994.1899.5596.7199.4310095.9795.55
SS-MambaTraining samples ratio=5%2026.0495.7898.3194.5195.8192.5499.799.141009999.9899.7299.99
SPP-DCNNTime=333.92017.0594.8893.2993.21—————————
SS-DCNNTime=266.112017.0593.3492.291.95—————————
SpectralFormerTraining samples ratio=5%2026.0493.1790.2791.2396.995.5978.986.3193.9182.5488.995.1793.97
3D-CNNTraining samples ratio=5%2026.0492.0190.4590.8793.394.2190.1991.2995.4793.8681.8480.3692.58
2D-CNNTraining samples ratio=5%2026.0490.1987.3187.5293.2894.975.5593.8797.8870.0570.9290.9690.78
SVMTraining samples ratio=5%2026.0482.7671.4476.2891.2297.7834.9581.5598.6941.532.1789.7496.87