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.
Daifeng et al. (Mon,) studied this question.