A Siamese Network via Cross-Domain Robust Feature Decoupling for Multi-Source Remote Sensing Image Registration

Image registration is a prerequisite for many multi-source remote sensing image fusion applications. However, due to differences in imaging factors such as sensor type, imaging time, resolution, and viewing angle, multi-source image registration faces challenges of multidimensional coupling such as radiation, scale, and directional differences. To address this issue, this paper proposes a Siamese network based on cross-domain robust feature decoupling as an image registration framework (CRS-Net), aiming to improve the robustness of multi-source image features across domains, scales, and rotations. Firstly, we design Siamese multiscale encoders and introduce a rotation-invariant convolutional layer without additional training parameters, achieving natural invariance to any rotation. Secondly, we propose a modality-independent decoder that utilizes the self-similarity of feature neighborhoods to excavate stable high-order structural information. Thirdly, we introduce cluster-aware contrastive constraints to learn discriminative and stable keypoint pairs. Finally, we design three multi-source remote sensing datasets and conduct sufficient experiments. Numerous experimental results show that our proposed method outperforms other SOTA methods and achieves more accurate registration in complex multi-source remote sensing scenes.