CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory
This work addresses the challenge of balancing performance and efficiency in remote sensing change detection for applications like environmental monitoring, though it is incremental in improving existing methods.
The paper tackles the problem of remote sensing change detection by proposing CDXLSTM, which integrates spatial-temporal context with linear computational complexity and global perception, achieving state-of-the-art performance on three benchmark datasets.
In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balanced consideration of performance and efficiency. CNNs lack global context, Transformers are computationally expensive, and Mambas face CUDA dependence and local correlation loss. In this paper, we propose CDXLSTM, with a core component that is a powerful XLSTM-based feature enhancement layer, integrating the advantages of linear computational complexity, global context perception, and strong interpret-ability. Specifically, we introduce a scale-specific Feature Enhancer layer, incorporating a Cross-Temporal Global Perceptron customized for semantic-accurate deep features, and a Cross-Temporal Spatial Refiner customized for detail-rich shallow features. Additionally, we propose a Cross-Scale Interactive Fusion module to progressively interact global change representations with spatial responses. Extensive experimental results demonstrate that CDXLSTM achieves state-of-the-art performance across three benchmark datasets, offering a compelling balance between efficiency and accuracy. Code is available at https://github.com/xwmaxwma/rschange.