CA-Mamba: A Change-Aware Mamba Decoding Framework for Remote Sensing Binary Change Detection

Remote sensing binary change detection aims to identify real changes in bi-temporal images while suppressing pseudo-change responses induced by complex backgrounds and temporal variations in otherwise unchanged regions. Although existing state-space-based methods incorporate cross-temporal interaction, integrating explicit difference priors into state-space parameter generation and subsequent boundary-aware decoding remains an important design problem. To address this problem, CA-Mamba, a change-aware Mamba decoding framework, is proposed. An Asymmetric Change-Aware State-Space Mixer (ACSSM) is introduced to condition state-space parameter generation on bi-temporal difference priors and to modulate the two temporal branches asymmetrically. The Cascaded Difference Prior Module (CDPM), the Change Boundary Enhancement (CBE) module, and the Change Representation Constructor (CRC) are further integrated into a unified pipeline for difference-prior generation and propagation, change-aware mixing, boundary enhancement, and region–boundary collaborative representation construction. Experiments on four public datasets, namely, LEVIR-CD, WHU-CD, CDD, and CLCD, show competitive detection performance. Ablation results support the use of signed-difference pathways and the overall CRC design under the evaluated settings. The efficiency analysis characterizes the trade-offs among detection accuracy, reported computational cost, inference latency, and parameter storage.