CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory

TL;DR

This paper introduces CDXLSTM, a novel change detection method for remote sensing that combines global context perception with computational efficiency, outperforming existing approaches on benchmark datasets.

Contribution

The paper proposes a new XLSTM-based feature enhancement layer and a cross-scale interactive fusion module, improving accuracy and efficiency in remote sensing change detection.

Findings

Achieves state-of-the-art performance on three benchmark datasets.

Balances efficiency and accuracy better than CNNs, Transformers, and Mambas.

Provides a scalable and interpretable framework for remote sensing change detection.

Abstract

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.