Zhou, R.; Wang, G.; Xu, H.; Zhang, Z. A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control. Remote Sens.2023, 15, 4941.
Zhou, R.; Wang, G.; Xu, H.; Zhang, Z. A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control. Remote Sens. 2023, 15, 4941.
Zhou, R.; Wang, G.; Xu, H.; Zhang, Z. A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control. Remote Sens.2023, 15, 4941.
Zhou, R.; Wang, G.; Xu, H.; Zhang, Z. A Sub-Second Method for SAR Image Registration Based on Hierarchical Episodic Control. Remote Sens. 2023, 15, 4941.
Abstract
For Synthetic Aperture Radar (SAR) image registration, successive processes following feature extraction are required by both the traditional feature-based method and the deep learning method. Among these processes, the feature matching process—whose time and space complexity are related to the number of feature points extracted from sensed and reference images, as well as the dimension of feature descriptors—proves to be particularly time-consuming. Additionally, the successive processes introduce data sharing and memory occupancy issues, requiring an elaborate design to prevent memory leaks. To address these challenges, this paper introduces the OptionEM-based reinforcement learning framework to achieve end-to-end SAR image registration. This framework outputs registered images directly without requiring feature matching and calculation of the transformation matrix, leading to significant processing time savings. The Transformer architecture is employed to learn image features, while a correlation network is introduced to learn the correlation and transformation matrix between image pairs. Reinforcement learning, as a decision process, can dynamically correct errors, making it more efficient and robust compared to supervised learning mechanisms like deep learning. We present a hierarchical reinforcement learning framework combined with episodic memory to mitigate the inherent problem of invalid exploration in generalized reinforcement learning algorithms. This approach effectively combines coarse and fine registration, further enhancing training efficiency. Experiments conducted on three sets of SAR images, acquired by TerraSAR-X and Sentinel-1A, demonstrate that the proposed method’s average runtime is sub-second, achieving subpixel registration accuracy.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
