A Light-weight Transformer-based Self-supervised Matching Network for Heterogeneous Images

arXiv:2404.19311v1 Announce Type: new
Abstract: Matching visible and near-infrared (NIR) images remains a significant challenge in remote sensing image fusion. The nonlinear radiometric differences between heterogeneous remote sensing images make the image matching task even more difficult. Deep learning has gained substantial attention in computer vision tasks in recent years. However, many methods rely on supervised learning and necessitate large amounts of annotated data. Nevertheless, annotated data is frequently limited in the field of remote sensing image matching. To address …

abstract arxiv attention challenge computer computer vision cs.cv cs.mm deep learning differences fusion however image images light near network sensing tasks transformer type vision