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 have quadratic computational complexity, and Mambas are restricted by CUDA acceleration. In this paper, we propose CDXFormer, 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 CDXFormer 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.

本研究解决了当前遥感变化检测方法在性能与效率之间平衡不足的问题。提出的CDXFormer利用基于XLSTM的特征增强层，结合线性计算复杂性的优势和全局上下文感知，显著提升了遥感变化检测的准确性和效率。实验证明，CDXFormer在多个基准数据集上实现了最先进的性能，具有重要的实际应用价值。

CDXFormer：利用扩展长短期记忆提升遥感变化检测