Optical and SAR image change detection based on a symmetric network

Compared with homogeneous image change detection (homo-CD), Change Detection (CD) of optical images and SAR images offers the advantage of utilizing complementary information from different types of data. This advantage 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 and SAR images prevent direct comparison of bitemporal images for CD. Existing methods for optical image and SAR image CD 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 study, we propose a Symmetric Change Detection Network (SCDN) that addresses the difference in imaging features between optical and SAR images by mapping them to a common feature space for comparison. The SCDN is initialized and optimized using similarity measurement, and it subsequently maps the optical and SAR images to a similar feature space for change information extraction. The proposed method consists of several steps. First, 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 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 multitemporal change vectors. To validate the proposed method, we conduct experiments using three sets of images, namely, Google Earth, Landsat-8, and Sentinel-1 images. Comparative analysis with five state-of-the-art methods reveals that the proposed method achieves an increase of at least 4.02% in the kappa coefficient while reducing the running time by at least 30.79%. In this study, we introduce SCDN, a CD method for optical and SAR images. Experimental results demonstrate its effectiveness in achieving relatively high precision and efficiency compared with existing methods. © 2024 Science Press. All rights reserved.