相比同源影像，光学影像和合成孔径雷达（Synthetic Aperture Radar，SAR）影像变化检测具有充分利用不同类型数据之间的互补信息、发挥其各自优势的优点，已成为遥感领域的研究热点，在应急灾害监测等方面具有广阔的应用前景。然而，光学影像和SAR影像的成像特征差异导致无法直接对比双时相影像提取变化信息，现有光学影像和SAR影像变化检测方法对特征空间统一的精度与效率不高。对此，本文认为光学影像和SAR影像间的差异主要由成像特征差异导致，因此可以通过特征空间变换，将影像映射到同一特征空间进行比较。由此提出了一种新的对称网络结构，通过基于相似性度量的网络初始化及优化，将光学影像和SAR影像映射到近似的特征空间中进行比较并提取变化信息。首先度量对称网络提取的多组特征之间的相似性，利用相似性最大特征组对应的网络权重实现网络初始化，引导光学影像和SAR影像特征映射。然后通过相似性优化学习将光学影像和SAR影像映射到同一特征空间进行直接对比，并对多时相特征变化向量进行聚类分析以区分变化类型。本文利用3组光学影像和SAR影像数据集（Google Earth影像、Landsat-8影像和哨兵1号影像）的实验结果表明：相对于现有方法，本文方法的卡帕系数（Kappa Coefficient，KC）至少提高了4.02%，且运行时间至少降低了30. 79%，有效提高了光学影像和SAR影像变化检测的精度与效率。

Objective: Change detection (CD) of optical images and SAR images, as compared to homogeneous images CD (homo-CD), offers the advantage of utilizing complementary information from different types of data. This has made it a research hotspot in the field of remote sensing image processing and holds promise for emergency disaster monitoring. However, the differences in imaging mechanisms between optical images and SAR images prevent direct comparison of bitemporal images for change detection. Existing methods for optical image and SAR image change detection still face certain challenges. Methods aiming to unify the feature space of optical and SAR images often suffer from issues such as low mapping precision and efficiency. In this paper, we propose a symmetric change detection network (SCDN) that addresses the difference in imaging features between optical images and SAR images by mapping them to a common feature space for comparison. The SCDN is initialized and optimized using similarity measurement and subsequently maps the optical images and SAR images to a similar feature space for change information extraction. Method: The proposed method consists of several steps. Firstly, the similarity between multiple sets of features generated by the symmetrical network is measured, and the weights corresponding to the most similar features are used to initialize the network. This initialization guides the network to map optical image and SAR image features. Subsequently, the SCDN maps the optical images and SAR images into the same feature space using similarity optimal learning, enabling direct comparison. Finally, change types are determined by clustering the multi-temporal change vectors. Results: To validate the proposed method, we conducted experiments using three sets of images, namely Google Earth, Landsat-8, and Sentinel-1 images. Comparative analysis with five state-of-the-art methods revealed that the proposed method achieved an increase of at least 4.02% in the Kappa Coefficient (KC), while reducing the running time by at least 30.79%. Conclusion: In this paper, we introduced SCDN, a change detection method for optical images and SAR images. Experimental results demonstrate its effectiveness in achieving relatively high precision and efficiency compared to existing methods.