Multimodal Remote Sensing Classification on Berlin 100 samples per class (train) benchmark leaderboard · SOTA2 Research

Multimodal Remote Sensing Classification on Berlin 100 samples per class (train)

89.87Class 1 AccuracyMSFMamba

Evaluation Results

Method	Links
MSFMamba2025.09		89.87	68.73	67.52	84.04	96.66	83.18	60.04	85.48	74.36	79.44	64.29
NCGLF2025.09		89.86	67.63	67.65	85.69	95.27	86.69	62.16	94.79	74.24	81.22	64.55
HLMamba2025.09		89.66	66.93	67.13	80.8	92.81	80.83	54.02	92.79	72.44	78.12	61.84
UACL2025.09		88.93	72.06	66.99	82.55	88.96	53	21.66	95.05	72.9	61.26	71.15
SS-MAE2025.09		88.14	67.46	68.08	87.47	93.04	83.88	60.98	93.39	73.93	80.31	63.76
MACN2025.09		84.96	65.84	64.94	86.72	94.37	85.02	52.93	80.14	71.85	76.86	61.16
BDGF2025.09		82.34	70.87	76.81	85.97	96.12	74.95	57.95	94.49	75.11	79.94	64.73
CALC2025.09		81.65	71.16	64.68	84.25	94.01	84.97	51.31	92.82	74.29	78.11	63.72
AsyFFNet2025.09		81.29	69.62	62.05	82.04	93.06	74.09	60.12	91.19	73.07	76.68	61.83
Fusion-HCT2025.09		79.67	68.04	64.73	89.51	96.09	80.33	57.2	92.44	73.18	78.5	62.64