Optical and SAR Image Registration Based on Pseudo-SAR Image Generation Strategy

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 14; p. 3528
• Main Authors: Hu, Canbin, Zhu, Runze, Sun, Xiaokun, Li, Xinwei, Xiang, Deliang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2023
• Subjects: Algorithms; Artificial intelligence; Comparative analysis; data collection; Deep learning; image analysis; image generation strategy; Image processing; Image registration; Methods; Neural networks; Outliers (statistics); pseudo-SAR; Radar imaging; Registration; Remote sensing; Synthetic aperture radar; Technology application; China
• DOI: 10.3390/rs15143528
• ISSN: 2072-4292

The registration of optical and SAR images has always been a challenging task due to the different imaging mechanisms of the corresponding sensors. To mitigate this difference, this paper proposes a registration algorithm based on a pseudo-SAR image generation strategy and an improved deep learning-based network. The method consists of two stages: a pseudo-SAR image generation strategy and an image registration network. In the pseudo-SAR image generation section, an improved Restormer network is used to convert optical images into pseudo-SAR images. An L2 loss function is adopted in the network, and the loss function fluctuates less at the optimal point, making it easier for the model to reach the fitting state. In the registration part, the ROEWA operator is used to construct the Harris scale space for pseudo-SAR and real SAR images, respectively, and each extreme point in the scale space is extracted and added to the keypoint set. The image patches around the keypoints are selected and fed into the network to obtain the feature descriptor. The pseudo-SAR and real SAR images are matched according to the descriptors, and outliers are removed by the RANSAC algorithm to obtain the final registration result. The proposed method is tested on a public dataset. The experimental analysis shows that the average value of NCM surpasses similar methods over 30%, and the average value of RMSE is lower than similar methods by more than 0.04. The results demonstrate that the proposed strategy is more robust than other state-of-the-art methods.