Deep descriptors have advantages over handcrafted descriptors on local image patch matching. However, due to the complex imaging mechanism of remote sensing images and the significant differences in appearance between multi-modal images, existing deep learning descriptors are unsuitable for multi-modal remote sensing image registration directly. To solve this problem, this article proposes a deep feature correlation learning network (Cnet) for multi-modal remote sensing image registration. First, Cnet builds a feature learning network based on the deep convolutional network with the attention learning module, to enhance feature representation by focusing on meaningful features. Second, this article designs a novel feature correlation loss function for Cnet optimization. It focuses on the relative feature correlation between matching and nonmatching samples, which can improve the stability of network training and decrease the risk of overfitting. In addition, the proposed feature correlation loss with a scale factor can further enhance network training and accelerate network convergence. Extensive experimental results on image patch matching (Brown, HPatches), cross-spectral image registration (VIS-NIR), multi-modal remote sensing image registration, and single-modal remote sensing image registration have demonstrated the effectiveness and robustness of the proposed method.

中文翻译：

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多模态遥感图像配准的深度特征相关学习


在局部图像块匹配方面，深度描述符比手工描述符具有优势。然而，由于遥感图像的成像机制复杂，且多模态图像之间的外观差异显着，现有的深度学习描述符不适合直接用于多模态遥感图像配准。为了解决这个问题，本文提出了一种用于多模态遥感图像配准的深度特征相关学习网络（Cnet）。首先，Cnet基于深度卷积网络和注意力学习模块构建了一个特征学习网络，通过关注有意义的特征来增强特征表示。其次，本文为 Cnet 优化设计了一种新颖的特征相关损失函数。它着眼于匹配和非匹配样本之间的相对特征相关性，可以提高网络训练的稳定性，降低过拟合的风险。此外，所提出的具有比例因子的特征相关损失可以进一步增强网络训练并加速网络收敛。图像块匹配（Brown、HPatches）、跨光谱图像配准（VIS-NIR）、多模态遥感图像配准和单模态遥感图像配准的大量实验结果证明了该方法的有效性和鲁棒性。