Image registration based on the deep learning framework centers on image feature extraction, leveraging booming neural network architectures such as the Transformer. Previous research has indicated that applying deep neural networks to the registration of complex and diverse SAR image pairs can yield more-accurate matching features compared to manually designed feature extraction algorithms, showcasing their promising performance and applicability [7,13,14]. These methods require a sufficient number of samples for training. However, several challenges remain, including the limited availability of publicly accessible datasets, the scarcity of labeled data [15,16], the substantial computational and time costs during the training phase, and the need for high-performance computer hardware. Moreover, local similarities may lead to mistaken matches. Addressing these challenges represents critical research areas when applying deep learning to SAR image registration.

Neural-network-based SAR image registration [17] falls under the umbrella of feature-based registration [7], transcending the limitations of manually designed features. It can extract multi-level features that reflect distributional, structural, and semantic characteristics. Various researchers have explored this approach, employing methods such as correlating coefficients and neural networks [18,19], utilizing Deep Convolutional Networks (CNNs) and Conditional Generative Adversarial Networks (CGANs) to extract geographic features [20], applying Pulse-Coupled Neural Networks (PCNNs) [21] for edge information [22], and combining SIFT algorithms with deep learning [23]. Fang Shang [24] constructed position vectors and change vectors that cleverly characterize image pixels and classified Polarimetric Synthetic Aperture Radar (PolSAR) images of complex terrain by a Quaternion Neural Network (QNN), which is not influenced by height information. Moreover, advanced techniques integrate self-learning with SIFT feature points for near-subpixel-level registration [7], employ deep forest models to enhance robustness [13], utilize unsupervised learning frameworks for multiscale registration [25,26,27], and leverage Transformer networks for efficient and accurate registration [28,29,30,31,32,33]. Deng, X. [13] employed a unique approach where each key point serves as a distinct class in the design of their multi-class model. This approach effectively circumvents the challenge of constructing matched-point pairs typically encountered in two-classification registration models. In a similar vein, S. Mao [31] introduced an adaptive self-supervised SAR-image-registration method that achieved comparable results. Meanwhile, Li, B. [29] presented a novel Siamese Dense Capsule Network designed to facilitate a more-even distribution of correctly matched keypoint pairs in SAR images featuring complex scenes. Fan, Y. [28] introduced an advanced and high-precision dense matching technique, specifically tailored for registering SAR images in situations characterized by weak texture conditions. The approaches of B. Zou [34] and Ming Zhao [35] involve the adoption of a pseudo-label-generation method, eliminating the need for additional annotations. Y. Ye [26] and D. Quan [36] separately built coarse-to-fine deep learning image registration framework based on stacking several deep models, which can significantly improve the multimodal image registration performances.

In summary, deep-learning-based registration methods for SAR images can leverage multi-level, latent, and multi-structural features to capture complex data variations. They guide feature extraction using registration results, eliminating the need for manually set metrics. These methods have demonstrated favorable accuracy and applicability. However, they require a substantial number of training samples and high computational power during the training phase.

Regarding registration or matching, it plays a significant role in the entire image registration process. This process is used to identify misregistrations between two images or two patches, and for two images, it detects their mapping matrix, ultimately transforming one image to match the other. When it comes to two patches cropped from key points, matching classification performs well. Quan et al. [37] introduced a deep feature Correlation learning network (Cnet) along with a novel feature correlation loss function for multi-modal remote sensing image registration. The experiments demonstrated that the well-designed loss function improved the stability of network training and decreased the risk of overfitting. Li, L. [38] and D. Xiang [39] utilized networks to extract feature information and generate descriptors, which can be used to obtain more-correct matching point pairs.

Blundell and colleagues introduced the Model-Free Episodic Control (MFEC) algorithm [40] as one of the earliest episodic reinforcement learning algorithms. In comparison to traditional parameter-based deep reinforcement learning methods, MFEC employs non-parametric Episodic Memory for value function estimation, resulting in higher sample efficiency compared to DQN algorithms. Neural Episodic Control (NEC) [41] introduced a differentiable neural dictionary to store episodic memories, enabling the estimation of state–action value functions based on the similarity between stored neighboring states.

Savinov et al. [42] utilized Episodic Memory to devise a curiosity-driven exploration strategy. Episodic Memory DQN (EMDQN) [43] combined parameterized neural networks with non-parametric Episodic Memory, enhancing the generalization capabilities of Episodic Memory. Generalizable Episodic Memory (GEM) [44] parameterized the memory module using neural networks, further enhancing the generalization capabilities of Episodic Memory algorithms. Additionally, GEM extended the applicability of Episodic Memory to continuous action spaces.

These algorithms represent significant advancements in the field of episodic reinforcement learning, offering improved memory and learning strategies that contribute to more-effective and -efficient training processes.