Towards Remote Sensing Change Detection with Neural Memory
This work improves change detection for environmental monitoring and urban planning by introducing a novel method that balances accuracy and efficiency, though it is incremental as it builds on existing Titans-based approaches.
The paper tackles the problem of remote sensing change detection by addressing the trade-off between capturing long-range dependencies and computational efficiency, resulting in a new framework called ChangeTitans that achieves state-of-the-art results, such as 84.36% IoU and 91.52% F1-score on the LEVIR-CD dataset.
Remote sensing change detection is essential for environmental monitoring, urban planning, and related applications. However, current methods often struggle to capture long-range dependencies while maintaining computational efficiency. Although Transformers can effectively model global context, their quadratic complexity poses scalability challenges, and existing linear attention approaches frequently fail to capture intricate spatiotemporal relationships. Drawing inspiration from the recent success of Titans in language tasks, we present ChangeTitans, the Titans-based framework for remote sensing change detection. Specifically, we propose VTitans, the first Titans-based vision backbone that integrates neural memory with segmented local attention, thereby capturing long-range dependencies while mitigating computational overhead. Next, we present a hierarchical VTitans-Adapter to refine multi-scale features across different network layers. Finally, we introduce TS-CBAM, a two-stream fusion module leveraging cross-temporal attention to suppress pseudo-changes and enhance detection accuracy. Experimental evaluations on four benchmark datasets (LEVIR-CD, WHU-CD, LEVIR-CD+, and SYSU-CD) demonstrate that ChangeTitans achieves state-of-the-art results, attaining \textbf{84.36\%} IoU and \textbf{91.52\%} F1-score on LEVIR-CD, while remaining computationally competitive.