CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model

Zhenkai Wu, Xiaowen Ma, Kai Zheng, Rongrong Lian, Yun Chen, Zhenhua Huang, Wei Zhang, Siyang Song

Abstract: Mamba, with itsadvantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions. However, existing remote sensing change detection (RSCD) approaches based on Mamba frequently struggle to effectively perceive the inherent locality of change regions as they direct flatten and scan RS images (i.e., the features of the same region of changes are not distributed continuously within the sequence but are mixed with features from other regions throughout the sequence). In this article, we propose a novel locally adaptive SSM-based approach, termed CD-Lamba, which effectively enhances the locality of change detection while maintaining global perception. Specifically, our CD-Lamba includes a locally adaptive state-space scan (LASS) strategy for locality enhancement, a cross-temporal state-space scan strategy for bitemporal feature fusion, and a window shifting and perception mechanism to enhance interactions across segmented windows. These strategies are integrated into a multiscale cross-temporal LASS module to effectively highlight changes and refine changes’ representations feature generation. CD-Lamba significantly enhances local–global spatio-temporal interactions in bitemporal images, offering improved performance in RSCD tasks. Extensive experimental results show that CD-Lamba achieves state-of-the-art performance on four benchmark datasets with a satisfactory efficiency-accuracy tradeoff.

External IDs:doi:10.1109/jstars.2025.3650075