Foundation Model-Based Deformable Registration of Multi-Modal Remote Sensing Images

Deformable image registration and correspondence play a critical role in multi-modal remote sensing (MRS) image analysis. However, challenges such as domain gaps and heterogeneous noise in MRS images often hinder performance. Existing methods typically address these issues through carefully designed strategies for image style separation and task-specific feature extraction. In this paper, we introduce an unsupervised domain-adaptive registration framework for establishing semantic correspondences in MRS images. Our approach leverages frozen pre-trained foundation models, DINOv2 and Stable Diffusion, to extract domain-adaptable features, effectively addressing sensor-specific radiometric and geometric distortions. To favor the semantically rich and domain-agnostic feature channels, we propose a learnable Channel Attention Adapter (CAA) that captures channel-wise dependencies. Additionally, we introduce a neural displacement field (DF) decoder, which eliminates the need for computationally expensive nearest neighbor searching or iterative optimization. We conduct extensive experiments to demonstrate the effectiveness of the proposed CAA-based feature enhancement and the neural DF decoder in deformable registration of MRS images. Our method outperforms state-of-the-art approaches on publicly available MRS image datasets. The code is available at https://github.com/orgxicv/CA-DF.