用改进的LSTM提升遥感变化检测的精度与效率
CDXLSTM: Boosting Remote Sensing Change Detection with Extended Long Short-Term Memory
- 提出XLSTM增强层，融合全局上下文与线性计算复杂度
- 在三个基准数据集上达到顶尖性能，兼顾准确率与速度
- 适合需要高效高精度变化检测的遥感应用
在复杂场景和多变条件下，有效整合时空上下文对精准识别变化至关重要。然而，现有遥感变化检测方法在性能与效率之间缺乏平衡：CNN缺乏全局上下文，Transformer计算开销大，Mamba存在CUDA依赖和局部相关性损失问题。本文提出CDXLSTM，核心为基于XLSTM的特征增强层，兼具线性计算复杂度、全局上下文感知与强可解释性。具体设计了针对语义精确深层特征的跨时域全局感知器，以及针对细节丰富的浅层特征的跨时域空间精炼器；并引入跨尺度交互融合模块，逐步融合全局变化表征与空间响应。大量实验表明，CDXLSTM在三个基准数据集上均达到领先水平，实现精度与效率的优异平衡。代码已开源：https://github.com/xwmaxwma/rschange。
原文摘要 · Abstract (English)
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.
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