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
this challenge, this paper proposes a novel keypoint descriptor approach that
obtains robust feature descriptors via a self-supervised matching network. A
light-weight transformer network, termed as LTFormer, is designed to generate
deep-level feature descriptors. Furthermore, we implement an innovative triplet
loss function, LT Loss, to enhance the matching performance further. Our
approach outperforms conventional hand-crafted local feature descriptors and
proves equally competitive compared to state-of-the-art deep learning-based
methods, even amidst the shortage of annotated data.

提出一种新的关键点描述方法，通过自监督匹配网络获取稳健的特征描述子。设计了一种轻量级变换网络 LTFormer 生成深层特征描述子，并通过创新的三元组损失函数 LT Loss 进一步提高匹配性能，相比手工设计的本地特征描述子表现更好，即使数据有限，也与最先进的基于深度学习的方法相媲美。