Fourier Domain Adaptive Multi-Modal Remote Sensing Image Template Matching Based on Siamese Network

Multi-modal remote sensing image template matching is a meaningful and crucial topic in remote sensing image processing. However, due to different imaging mechanisms, there are significant nonlinear radiometric variations among multi-modal remote sensing images, increasing the matching challenge and leading to poor matching performances. To tackle this issue, this paper proposes a Fourier Domain Adaptive Network (FDANet) for multi-modal remote sensing image matching. Firstly, FDANet randomly swaps the low-frequency spectrum information between multi-modal images through the Fourier transform to reduce differences among multi-modal images, enhancing network adaptability to different image modalities and improving the multi-modal image matching performance. Secondly, FDANet extracts domain-invariant features from the transformed images through a deep Siamese network. After that, FDANet performs template matching and achieves high-precision multi-modal remote sensing image matching. In addition, we adopt the contrastive learning loss to optimize the FDANet. Extensive experiments on multi-modal remote sensing image matching demonstrate the effectiveness and advantages of the proposed FDANet.