PolSAR Image Classification on Flevoland (test) benchmark leaderboard · SOTA2 Research

PolSAR Image Classification on Flevoland (test)

99.65Water AccuracyDDF2Pol

Evaluation Results

Method	Links
DDF2Pol2026.04		99.65	99.52	98.49	95.54	97.96	93.55	96.7	99.29	99.26	98.45	98.2	98.55	99.6	98.3	100	98.16	98.2	97.99
3D-CNN2026.04		99.05	98.45	96.97	84.84	91.23	82.55	71.2	98.15	98.77	25.82	85.44	98.31	97.53	98.78	100	89.7	88.47	88.73
CV-2D-3D2026.04		98.41	93.42	97.49	87.44	81.95	89.22	94.16	95.56	98.45	98.13	96.42	91.81	95.1	97.76	100	93.6	94.35	93.01
HybridCVNet2026.04		97.55	99.6	97.81	97.84	94.69	93.84	90.14	98.81	99.6	97.55	96.45	99.21	99.59	97.22	100	96.7676	97.3313	96.4711
WaveletCNN2026.04		97.25	97.63	98.63	83.92	95.65	91.54	77.84	96.4	99.39	98.89	96.47	84.29	99.12	99.39	99.48	93.78	94.39	93.21
PolSARFormer2026.04		97.04	97.22	97.22	90.29	94.48	91.76	84.7	98.42	96.81	97.97	90.67	95.95	97.44	98.29	100	94.41	95.22	93.9
3D-CV-CNN2026.04		95.68	99.32	91.38	72.98	94.34	90.71	87.28	90.4	98.47	95.79	88.57	88.5	95.12	98.05	98.79	92.17	92.36	91.45