RMSO-ConvNeXt: A Lightweight CNN Network for Robust SAR and Optical Image Matching Under Strong Noise Interference

Synthetic aperture radar (SAR) and optical images provide complementary imaging information, and their joint application holds broad prospects in military reconnaissance and aircraft visual navigation, with the key task of achieving accurate image matching. However, due to significant differences in imaging characteristics and the presence of strong noise interference in complex electromagnetic environments, SAR imaging may face serious challenges in matching with optical images. In addition, limited hardware resources on airborne platforms make it difficult for existing matching algorithms to meet the requirements for online real-time matching. In this article, a lightweight, high-performance, and robust method for robust SAR and optical image matching is proposed. The main contributions include the design of a lightweight convolutional neural network (CNN) and an SAR image random mask noise-resistant feature reconstruction network (named RMSO-ConvNeXt). First, a lightweight pseudo-Siamese dense feature extraction network module was explored for pixel-wise feature extraction of SAR and optical images, which can efficiently extract common features between heterogeneous images. Second, an SAR random noise mask feature reconstruction network was designed to address noise interference in SAR images, and a new joint loss function was constructed to significantly enhance the model’s ability to resist strong noise interference. Furthermore, a high-quality large-scale SAR and optical dataset has been made publicly available to contribute to the advancement and application of image matching. Finally, extensive experiments demonstrate that compared to current state-of-the-art matching methods, the proposed approach exhibits stronger robustness, better matching performance, and smaller model parameters. The code and dataset are shared on https://github.com/yeyuanxin110/RMSO-ConvNeXt.