Self-supervised rotation-invariant descriptors for heterogeneous remote sensing image matching

We propose self-supervised rotation-invariant descriptors based on mixed rotation-equivariant convolutional neural networks (CNNs) (MRDes) for heterogeneous remote sensing image matching between visible (VIS) and near-infrared (NIR) images. Existing methods struggle with large rotation variations for VIS-NIR image matching, particularly when georeferenced information is unavailable or inaccurate, limiting their practical applicability. To address this, MRDes employs rotation-equivariant CNNs to extract equivariant features and construct robust rotation-invariant descriptors. Specifically, we design a mixed learning strategy that integrates explicit and implicit equivariance to optimize feature representations, while a contrastive loss enhances their discriminability by refining the distances between positive and negative samples. Experimental results on benchmark datasets demonstrate that MRDes significantly outperforms state-of-the-art methods, achieving a 70.9% improvement in matching success rate over XoFTR and exhibiting strong generalization to unseen UAV data.