南京航空航天大学学报（英文版）2025，Vol.42Issue(6)：728-748,21.DOI:10.16356/j.1005-1120.2025.06.003
DFFMamba:一种基于差异特征融合Mamba的新型遥感影像变化检测方法
DFFMamba:A Novel Remote Sensing Change Detection Method with Difference Feature Fusion Mamba
摘要
Abstract
Change detection(CD)plays a crucial role in numerous fields,where both convolutional neural networks(CNNs)and Transformers have demonstrated exceptional performance in CD tasks.However,CNNs suffer from limited receptive fields,hindering their ability to capture global features,while Transformers are constrained by high computational complexity.Recently,Mamba architecture,which is based on state space models(SSMs),has shown powerful global modeling capabilities while achieving linear computational complexity.Although some researchers have incorporated Mamba into CD tasks,the existing Mamba-based remote sensing CD methods struggle to effectively perceive the inherent locality of changed regions when flattening and scanning remote sensing images,leading to limitations in extracting change features.To address these issues,we propose a novel Mamba-based CD method termed difference feature fusion Mamba model(DFFMamba)by mitigating the loss of feature locality caused by traditional Mamba-style scanning.Specifically,two distinct difference feature extraction modules are designed:Difference Mamba(DMamba)and local difference Mamba(LDMamba),where DMamba extracts difference features by calculating the difference in coefficient matrices between the state-space equations of the bi-temporal features.Building upon DMamba,LDMamba combines a locally adaptive state-space scanning(LASS)strategy to enhance feature locality so as to accurately extract difference features.Additionally,a fusion Mamba(FMamba)module is proposed,which employs a spatial-channel token modeling SSM(SCTMS)unit to integrate multi-dimensional spatio-temporal interactions of change features,thereby capturing their dependencies across both spatial and channel dimensions.To verify the effectiveness of the proposed DFFMamba,extensive experiments are conducted on three datasets of WHU-CD,LEVIR-CD,and CLCD.The results demonstrate that DFFMamba significantly outperforms state-of-the-art CD methods,achieving intersection over union(IoU)scores of 90.67%,85.04%,and 66.56%on the three datasets,respectively.关键词
变化检测/状态空间模型/变化特征融合/深度学习/差异Mamba/局部差异Mamba/空间-通道序列建模SSMKey words
change detection/state space model(SSM)change feature fusion/deep learning/difference Mamba(DMamba)/local difference Mamba(LDMamba)/spatial-channel token modeling SSM(SCTMS)分类
天文与地球科学引用本文复制引用
彭代锋,董峰旭,管海燕..DFFMamba:一种基于差异特征融合Mamba的新型遥感影像变化检测方法[J].南京航空航天大学学报（英文版）,2025,42(6):728-748,21.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(Nos.42371449,41801386). （Nos.42371449,41801386）
