We propose an effective deep neural network aiming at remote sensing image registration problem. Unlike conventional methods doing feature extraction and feature matching separately, we pair patches from sensed and reference images, and then learn the mapping directly between these patch-pairs and their matching labels for later registration. This end-to-end architecture allows us to optimize the whole processing (learning mapping function) through information feedback when training the network, which is lacking in conventional methods. In addition, to alleviate the small data issue of remote sensing images for training, our proposal introduces a self-learning by learning the mapping function using images and their transformed copies. Moreover, we apply a transfer learning to reduce the huge computation cost in the training stage. It does not only speed up our framework, but also get extra performance gains. The comprehensive experiments conducted on seven sets of remote sensing images, acquired by Radarsat, SPOT and Landsat, show that our proposal improves the registration accuracy up to 2.4–53.7%.

我们针对遥感图像配准问题提出了一种有效的深度神经网络。与传统方法进行特征提取不同并分别进行特征匹配，我们将传感图像和参考图像中的补丁配对，然后直接学习这些补丁对及其匹配标签之间的映射，以供以后注册。这种端到端的体系结构使我们可以在训练网络时通过信息反馈优化整个处理过程（学习映射功能），而这是常规方法所缺乏的。另外，为了减轻遥感图像用于训练的小数据问题，我们的建议通过使用图像及其变换后的副本学习映射功能来引入自学习。此外，我们采用转移学习来减少训练阶段的巨大计算成本。它不仅可以加快我们的框架的速度，而且可以获得额外的性能提升。对七套遥感影像进行的综合实验，SPOT和Landsat的研究表明，我们的建议将注册准确率提高了2.4–53.7％。