Journal of Geo-information Science

MSF-UNet: A Mamba-based Spatial-Frequency Feature Fusion U-Net for Unsupervised Change Detection in SAR Images

ZHANG Yu, ZHUANG Huifu, ZHANG Xiang, TAN Zhixiang, LIU Yuhao, SHANG Jingjie, GUO Mingming

Journal of Geo-information Science, 2025, 27(9): 2213-2229. DOI: 10.12082/dqxxkx.2025.250269

方法	DSCF	Mamba	频率域特征编码器	总体误差/个	F1_Score/%	Kappa/%	参数量/M
M1	×	×	×	15 248	0.842	0.778	89.645
M2	√	×	×	10 183	0.893	0.851	89.645
M3	√	√	×	9 283	0.904	0.864	91.847
M4	√	√	√	6 738	0.928	0.898	108.847

Tab. 3 Evaluation metric results of ablation experiments on the Handan dataset

Other figure/table from this article

Fig. 1 The overall framework of the proposed method
Fig. 2 Flowchart of the DSCF method
Fig. 3 Structural diagram of the MSF-UNet model
Fig. 4 Images and reference change map of the Handan dataset
Fig. 5 Images and reference change map of the Yellow River dataset
Fig. 6 Impact analysis of threshold m on F1_Score of change detection results
Fig. 7 Change detection results of different methods on the Handan dataset
Tab. 1 Quantitative analysis results of different methods on the Handan dataset
Fig. 8 Change detection results of different methods on the Yellow River dataset
Tab. 2 Quantitative analysis results of different methods on the Yellow River dataset
Fig. 9 Change detection results of the ablation experiment on the Handan dataset
Tab. 4 Model parameter quantities and floating-point operation counts of different methods