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 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.
Abstract (translated)
匹配可见和近红外（NIR）图像仍然是遥感图像融合的一个显著挑战。异质遥感图像的非线性辐射差异使得图像匹配任务变得更加困难。近年来，计算机视觉任务在深度学习领域得到了很多关注。然而，许多方法依赖于监督学习，需要大量标记数据。然而，遥感图像匹配领域标记数据经常有限。为了应对这个挑战，本文提出了一种新的关键点描述方法，通过自监督匹配网络获得稳健的特征描述符。一种轻量级的Transformer网络，被称为LTFormer，被设计用于生成深层次特征描述。此外，我们还实现了一种创新的三元组损失函数，称为LT Loss，以提高匹配性能。我们的方法在传统手工局部特征描述符的基础上表现优异，并证明了与基于深度学习的先进方法在竞争中具有竞争力，即使在标记数据有限的情况下。
URL
https://arxiv.org/abs/2404.19311
