标题： Mamba-FCS：联合时空频特征融合、变化引导注意机制和SeK损失的遥感增强语义变化检测 显示英文标题

标题： Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing

摘要： 语义变化检测（SCD）从遥感图像需要平衡广泛的空域上下文、计算效率和对类别不平衡土地覆盖变化的敏感性。 虽然卷积神经网络在局部特征提取方面表现出色，但缺乏全局上下文，而Transformer在高计算成本下提供全局建模。 基于状态空间模型的最近Mamba架构通过线性复杂度和高效的长距离建模提供了有吸引力的解决方案。 在本研究中，我们引入了Mamba-FCS，一个基于视觉状态空间模型主干的SCD框架，包括一个联合空频融合块，结合对数幅度频域特征以增强边缘清晰度并抑制光照伪影，一个变化引导注意（CGA）模块，明确连接自然交织的BCD和SCD任务，以及一个为类别不平衡性能优化量身定制的分离Kappa（SeK）损失。 在SECOND和Landsat-SCD数据集上的广泛评估表明，Mamba-FCS达到了最先进的指标，SECOND数据集上88.62%的整体准确率，65.78%的F_scd，25.50%的SeK，Landsat-SCD数据集上96.25%的整体准确率，89.27%的F_scd，60.26%的SeK。 消融分析确认了每个新组件的独特贡献，定性评估突显了SCD的显著改进。 我们的结果强调了Mamba架构的显著潜力，通过所提出的技术增强，在遥感应用中设定了有效的和可扩展的语义变化检测的新基准。 完整的源代码、配置文件和预训练模型将在发表后公开。 显示英文摘要

摘要： Semantic Change Detection (SCD) from remote sensing imagery requires models balancing extensive spatial context, computational efficiency, and sensitivity to class-imbalanced land-cover transitions. While Convolutional Neural Networks excel at local feature extraction but lack global context, Transformers provide global modeling at high computational costs. Recent Mamba architectures based on state-space models offer compelling solutions through linear complexity and efficient long-range modeling. In this study, we introduce Mamba-FCS, a SCD framework built upon Visual State Space Model backbone incorporating, a Joint Spatio-Frequency Fusion block incorporating log-amplitude frequency domain features to enhance edge clarity and suppress illumination artifacts, a Change-Guided Attention (CGA) module that explicitly links the naturally intertwined BCD and SCD tasks, and a Separated Kappa (SeK) loss tailored for class-imbalanced performance optimization. Extensive evaluation on SECOND and Landsat-SCD datasets shows that Mamba-FCS achieves state-of-the-art metrics, 88.62% Overall Accuracy, 65.78% F_scd, and 25.50% SeK on SECOND, 96.25% Overall Accuracy, 89.27% F_scd, and 60.26% SeK on Landsat-SCD. Ablation analyses confirm distinct contributions of each novel component, with qualitative assessments highlighting significant improvements in SCD. Our results underline the substantial potential of Mamba architectures, enhanced by proposed techniques, setting a new benchmark for effective and scalable semantic change detection in remote sensing applications. The complete source code, configuration files, and pre-trained models will be publicly available upon publication.