多模态变化检测（MMCD）旨在从多模态遥感（RS）数据中识别变化区域，在土地利用监测、灾害评估及城市可持续发展等领域具有重要应用价值。然而，现有MMCD方法在跨模态交互与模态特异性特征挖掘方面存在局限，导致对细粒度变化信息建模不足，难以精准检测多模态数据中的语义变化。为解决上述问题，本文提出STSF-Net——一种面向光学与合成孔径雷达（SAR）图像的MMCD框架。STSF-Net联合建模模态特异性特征与时空共性特征，以增强变化表征能力：模态特异性特征用于捕获真实的语义变化信号，而时空共性特征则用于抑制由成像机制差异引发的伪变化。此外，我们引入一种光学与SAR特征融合策略，该策略依据预训练基础模型提取的语义先验，自适应调整各模态特征的重要性，实现语义引导下的多模态信息自适应融合。同时，我们构建了Delta-SN6数据集，这是首个公开可用的多类别MMCD基准数据集，包含甚高分辨率（VHR）全极化SAR与光学图像。在Delta-SN6、BRIGHT和Wuhan-Het数据集上的实验结果表明，本方法在平均交并比（mIoU）指标上分别较当前最优方法（SOTA）提升3.21%、1.08%和1.32%。相关代码与Delta-SN6数据集将发布于：https://github.com/liuxuanguang/STSF-Net。

Multimodal change detection (MMCD) identifies changed areas in multimodal remote sensing (RS) data, demonstrating significant application value in land use monitoring, disaster assessment, and urban sustainable development. However, literature MMCD approaches exhibit limitations in cross-modal interaction and exploiting modality-specific characteristics. This leads to insufficient modeling of fine-grained change information, thus hindering the precise detection of semantic changes in multimodal data. To address the above problems, we propose STSF-Net, a framework designed for MMCD between optical and SAR images. STSF-Net jointly models modality-specific and spatio-temporal common features to enhance change representations. Specifically, modality-specific features are exploited to capture genuine semantic change signals, while spatio-temporal common features are embedded to suppress pseudo-changes caused by differences in imaging mechanisms. Furthermore, we introduce an optical and SAR feature fusion strategy that adaptively adjusts feature importance based on semantic priors obtained from pre-trained foundational models, enabling semantic-guided adaptive fusion of multi-modal information. In addition, we introduce the Delta-SN6 dataset, the first openly-accessible multiclass MMCD benchmark consisting of very-high-resolution (VHR) fully polarimetric SAR and optical images. Experimental results on Delta-SN6, BRIGHT, and Wuhan-Het datasets demonstrate that our method outperforms the state-of-the-art (SOTA) by 3.21%, 1.08%, and 1.32% in mIoU, respectively. The associated code and Delta-SN6 dataset will be released at: https://github.com/liuxuanguang/STSF-Net.